{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "56210123",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51c3faaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "\n",
    "#Plots\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "#Machine Learning Libraries\n",
    "import jax\n",
    "from jax import jit, grad, vmap, value_and_grad\n",
    "from jax.config import config\n",
    "import jax.numpy as jnp\n",
    "import optax\n",
    "import jaxopt\n",
    "from typing import List, Dict, Tuple\n",
    "from pyDOE import lhs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6283d3e",
   "metadata": {},
   "source": [
    "# Auxiliary Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "063281c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Encode_Fourier(X,M,N):\n",
    "    x=X[0]\n",
    "    y=X[1]\n",
    "    P_x=2\n",
    "    P_y=2\n",
    "    n_num = jnp.arange(1, N+1)\n",
    "    m_num = jnp.arange(1, M+1)\n",
    "    n, m = jnp.meshgrid(n_num, m_num)\n",
    "    n=n.flatten()\n",
    "    m=m.flatten()\n",
    "    w_x = 2.0 * jnp.pi / P_x\n",
    "    w_y = 2.0 * jnp.pi / P_y    \n",
    "    out = jnp.hstack([jnp.cos(n* w_x * x)  * jnp.cos(m * w_y * y),\n",
    "                      jnp.cos(n * w_x * x) * jnp.sin(m * w_y * y),\n",
    "                      jnp.sin(n * w_x * x) * jnp.cos(m * w_y * y),])\n",
    "    return out\n",
    "def identity(X,X_min,X_max):\n",
    "    return X\n",
    "def MSE(pred,exact,weight=1):\n",
    "    return jnp.mean(weight*jnp.square(pred - exact))\n",
    "def relative_error2(pred,exact):\n",
    "    return np.linalg.norm(exact-pred,2)/np.linalg.norm(exact,2)\n",
    "#Initialization\n",
    "def glorot_normal(in_dim, out_dim):\n",
    "    glorot_stddev = np.sqrt(2.0 / (in_dim + out_dim))\n",
    "    W = glorot_stddev * jnp.array(np.random.normal(size=(in_dim, out_dim)))\n",
    "    return W\n",
    "def init_params(layers, Mod_MLP=True):\n",
    "    params = []\n",
    "    in_dim, out_dim = layers[0], layers[1]\n",
    "    U1=glorot_normal(in_dim, out_dim)\n",
    "    b1=jnp.zeros(out_dim)\n",
    "    U2=glorot_normal(in_dim, out_dim)\n",
    "    b2=jnp.zeros(out_dim)\n",
    "    for i in range(len(layers) - 1):\n",
    "        in_dim, out_dim = layers[i], layers[i + 1]\n",
    "        W = glorot_normal(in_dim, out_dim)\n",
    "        b = jnp.zeros(out_dim)\n",
    "        if i ==0:\n",
    "            params.append({\"W\": W, \"b\": b, \"U1\":U1, \"b1\":b1,\"U2\":U2, \"b2\":b2,})\n",
    "        else:\n",
    "            params.append({\"W\": W, \"b\": b})\n",
    "    return params\n",
    "\n",
    "def FCN_MMLP(params,X_in,M1,M2,activation,norm_fn):#Modified MLP\n",
    "    inputs =norm_fn(X_in,M1,M2)\n",
    "    # MMLP\n",
    "    U1=params[0][\"U1\"]\n",
    "    U2=params[0][\"U2\"]\n",
    "    b1=params[0][\"b1\"]\n",
    "    b2=params[0][\"b2\"]\n",
    "    U =activation(jnp.dot(inputs, U1) + b1)\n",
    "    V =activation(jnp.dot(inputs, U2) + b2)\n",
    "    for layer in params[:-1]:\n",
    "        outputs = activation(inputs @ layer[\"W\"] + layer[\"b\"]) \n",
    "        inputs  = jnp.multiply(outputs, U) + jnp.multiply(1 - outputs, V) \n",
    "    W = params[-1][\"W\"]\n",
    "    b = params[-1][\"b\"]\n",
    "    outputs = jnp.dot(inputs, W) + b\n",
    "    return outputs\n",
    "def plot3D_mat(x,y,F,f_names,window=4,font_size='10',x_label='x',y_label='y',cmap='rainbow',fig_width=6):\n",
    "  X,Y= x,y\n",
    "  fig,ax=plt.subplots(1,len(F),figsize=(fig_width*len(F),window))\n",
    "  plt.rcParams['font.size'] = font_size\n",
    "  for i in range(len(ax)):\n",
    "    cp = ax[i].contourf(X,Y, F[i],50,cmap=cmap)\n",
    "    fig.colorbar(cp) # Add a colorbar to a plot\n",
    "    ax[i].set_title(f_names[i])\n",
    "    ax[i].set_xlabel(x_label)\n",
    "    if i==0:\n",
    "        ax[i].set_ylabel(y_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "964d9e65",
   "metadata": {},
   "source": [
    "# Tunning Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cec69f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Architecture:\n",
      "[2, 128, 128, 128, 128, 128, 128, 1]\n"
     ]
    }
   ],
   "source": [
    "Mode='ADF'\n",
    "use_RBA=True\n",
    "Mod_MLP=True\n",
    "seed_np=5555\n",
    "\n",
    "#Model\n",
    "num_layer=6\n",
    "width_layer = 128 # neurons/layer\n",
    "layers = [2] + num_layer*[width_layer] + [1]\n",
    "print('Model Architecture:')\n",
    "print(layers)\n",
    "#Optimization\n",
    "num_epochs_adam =20*(10**3)\n",
    "num_epochs_lbfgsb =1*(10**3)\n",
    "substepts_lbfgs=5\n",
    "#Global weights\n",
    "lamB =100\n",
    "lamE =1\n",
    "#learning \n",
    "lr0=5*10**(-3)\n",
    "lrf=1*10**(-5)\n",
    "#RBA\n",
    "lr_lambdas_0=1*10**(-2)\n",
    "decay_rate=0.7\n",
    "decay_step=1000\n",
    "gamma=0.999\n",
    "lam_min=0.0\n",
    "init_zero=True\n",
    "#Number of training points\n",
    "N0 = 50\n",
    "N_b = 50\n",
    "nxb, nyb = (101,101)\n",
    "N_f = nxb*nyb\n",
    "#Fourier \n",
    "N=5\n",
    "M=5\n",
    "#Helmhotz Details\n",
    "a1 = 1.0\n",
    "a2 = 4.0\n",
    "ksq = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5550626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Settings\n",
    "if Mode.lower()  == 'fourier':\n",
    "    dim_in = N * M * 3\n",
    "    layers = [dim_in] + num_layer*[width_layer] + [1]\n",
    "    print('New Model Architecture:')\n",
    "    print(layers)\n",
    "    print('Mode:')\n",
    "    print(Mode)\n",
    "activation_fn=jnp.tanh\n",
    "if Mode.lower()=='adf':\n",
    "    norm_fn    = identity\n",
    "    Norm_metric1=lambda x: 0\n",
    "    Norm_metric2=lambda x: 0  \n",
    "elif Mode.lower()=='fourier':\n",
    "    norm_fn    = Encode_Fourier\n",
    "    Norm_metric1=lambda x: N\n",
    "    Norm_metric2=lambda x: M  \n",
    "# Errors\n",
    "Error_fn=MSE\n",
    "# Initialize NN\n",
    "np.random.seed(seed_np)\n",
    "params = init_params(layers) \n",
    "pinn_fn = FCN_MMLP\n",
    "#Optimizer\n",
    "optimizer_w = optax.adam(optax.exponential_decay(lr0, decay_step, decay_rate,))\n",
    "opt_state_w = optimizer_w.init(params)\n",
    "M1= Norm_metric1(1)\n",
    "M2= Norm_metric2(1)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "914bff94",
   "metadata": {},
   "source": [
    "## Generate Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2424041",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = np.array([-1.0])\n",
    "ub = np.array([1.0])\n",
    "rb = np.array([1.0])\n",
    "lftb = np.array([-1.0])\n",
    "nx, ny = (1001,1001)\n",
    "x = np.linspace(-1, 1, nx)\n",
    "y = np.linspace(-1, 1, ny)\n",
    "xv, yv = np.meshgrid(x,y)\n",
    "x = np.reshape(x, (-1,1))\n",
    "y = np.reshape(y, (-1,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "428d9ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exact solution to compute errors and evaluate performance\n",
    "Exact_u = np.sin(a1*np.pi*xv)*np.sin(a2*np.pi*yv)\n",
    "X,Y=np.meshgrid(x,y,indexing='ij')\n",
    "u=np.reshape(Exact_u ,(nx,ny)).T\n",
    "Exact=np.array([u,u]) #we store them this way only to plot them\n",
    "X_exact=np.hstack((X.flatten()[:,None],\n",
    "                   Y.flatten()[:,None],\n",
    "                   u.flatten()[:,None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cf7f23",
   "metadata": {},
   "source": [
    "# Training Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "530357df",
   "metadata": {},
   "outputs": [],
   "source": [
    "xb = np.linspace(-1, 1, N_b)\n",
    "yb = np.linspace(-1, 1, N_b)\n",
    "Xb, Yb = np.meshgrid(xb,yb)\n",
    "xb = np.reshape(xb, (-1,1))\n",
    "yb = np.reshape(yb, (-1,1))\n",
    "X_lb = np.hstack((Xb[0,:].flatten()[:,None],Yb[0,:].flatten()[:,None]))\n",
    "X_ub = np.hstack((Xb[-1,:].flatten()[:,None],Yb[-1,:].flatten()[:,None]))\n",
    "X_rb = np.hstack((Xb[:,0].flatten()[:,None],Yb[:,0].flatten()[:,None]))\n",
    "X_lftb = np.hstack((Xb[:,-1].flatten()[:,None],Yb[:,-1].flatten()[:,None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c945d1d1",
   "metadata": {},
   "source": [
    "# Collocation Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87610543",
   "metadata": {},
   "outputs": [],
   "source": [
    "xb = np.linspace(-1, 1, nxb)\n",
    "yb = np.linspace(-1, 1, nyb)\n",
    "Xb, Yb = np.meshgrid(xb,yb)\n",
    "xb = np.reshape(xb, (-1,1))\n",
    "yb = np.reshape(yb, (-1,1))\n",
    "X_f=np.hstack((Xb.flatten()[:,None],Yb.flatten()[:,None]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc09619",
   "metadata": {},
   "source": [
    "# Define data details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24380702",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_res    =X_f\n",
    "X_BCs_U  =X_ub\n",
    "X_BCs_L  =X_lb\n",
    "X_BCs_R  =X_rb\n",
    "X_BCs_Lf  =X_lftb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28ce12b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize = (6,6)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "plt.rcParams['font.size'] = '16'\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Scatter plots for each boundary condition or region with labels for the legend\n",
    "ax.scatter(X_BCs_L[:,0], X_BCs_L[:,1], c='b', s=5, label='Left Boundary')\n",
    "ax.scatter(X_BCs_U[:,0], X_BCs_U[:,1], c='r', s=5, label='Upper Boundary')\n",
    "ax.scatter(X_BCs_R[:,0], X_BCs_R[:,1], c='yellow', s=5, label='Right Boundary')\n",
    "ax.scatter(X_BCs_Lf[:,0], X_BCs_Lf[:,1], c='k', s=5, label='Lower Boundary')\n",
    "ax.scatter(X_res[:,0], X_res[:,1], c='g', s=0.05, label='Residual Points')\n",
    "\n",
    "# Adding a legend to the plot to differentiate the scatter points\n",
    "plt.legend()\n",
    "\n",
    "# Adding a title and axis labels\n",
    "plt.title('Domain Visualization')\n",
    "plt.xlabel('X Axis Label')\n",
    "plt.ylabel('Y Axis Label')\n",
    "\n",
    "# Displaying the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354aae62",
   "metadata": {},
   "source": [
    "# Initilize RBA Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f41147c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting Zero Initialization\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(seed_np)\n",
    "lambda_bcs=np.random.uniform(0,1,X_BCs_U.shape[0]*layers[-1]).reshape(X_BCs_U.shape[0],layers[-1])\n",
    "lambda_res=np.random.uniform(0,1,X_res.shape[0]*layers[-1]).reshape(X_res.shape[0],layers[-1])\n",
    "if init_zero:\n",
    "    print('Setting Zero Initialization')\n",
    "    lambda_bcs=lambda_bcs\n",
    "    lambda_res=lambda_res*0\n",
    "lambdas={\n",
    "         'BCs':lambda_bcs,\n",
    "         'Res':lambda_res}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bd73e3",
   "metadata": {},
   "source": [
    "# Loss Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1416b8f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Choosen Model: <function u_adf at 0x7f5d55766310>\n",
      "Weights for BCs and eq:0,1\n"
     ]
    }
   ],
   "source": [
    "def u_PINN(params):\n",
    "    u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "    u = lambda x: u_NN(x)[0]\n",
    "    return u    \n",
    "def u_adf(params):\n",
    "    u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "    u    = lambda x: (x[0]**2-1)*(x[1]**2-1)*u_NN(x)[0]   \n",
    "    return u   \n",
    "if Mode.lower()=='adf':\n",
    "    u_model=u_adf\n",
    "    lamB=0\n",
    "    #Def PDE\n",
    "    def PDE(params):\n",
    "        u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "        u    = lambda x: (x[0]**2-1)*(x[1]**2-1)*u_NN(x)[0]  \n",
    "        # derivatives\n",
    "        u_x              = lambda x: grad(u)(x)[0]\n",
    "        u_y              = lambda x: grad(u)(x)[1]\n",
    "        u_xx             = lambda x: grad(u_x)(x)[0]\n",
    "        u_yy             = lambda x: grad(u_y)(x)[1]\n",
    "        #Forcing Term\n",
    "        q                = lambda x:(- (a1*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) - \n",
    "                                    (a2*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) + \n",
    "                                        ksq*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]))\n",
    "        # equations\n",
    "        eq1 = (\n",
    "                        lambda x:  u_xx(x) + u_yy(x) + ksq*u(x) - q(x)\n",
    "        )\n",
    "        \n",
    "        return eq1\n",
    "else:\n",
    "    u_model=u_PINN\n",
    "    #Def PDE\n",
    "    def PDE(params):\n",
    "        u_NN = lambda x: pinn_fn(params, x,M1,M2,activation_fn,norm_fn)\n",
    "        u = lambda x: u_NN(x)[0]\n",
    "        # derivatives\n",
    "        u_x              = lambda x: grad(u)(x)[0]\n",
    "        u_y              = lambda x: grad(u)(x)[1]\n",
    "        u_xx             = lambda x: grad(u_x)(x)[0]\n",
    "        u_yy             = lambda x: grad(u_y)(x)[1]\n",
    "        #Forcing Term\n",
    "        q                = lambda x:(- (a1*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) - \n",
    "                                    (a2*jnp.pi)**2*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]) + \n",
    "                                        ksq*jnp.sin(a1*jnp.pi*x[0])*jnp.sin(a2*jnp.pi*x[1]))\n",
    "        # equations\n",
    "        eq1 = (\n",
    "                        lambda x:  u_xx(x) + u_yy(x) + ksq*u(x) - q(x)\n",
    "        )\n",
    "        \n",
    "        return eq1\n",
    "print('Choosen Model:',u_model)\n",
    "print(f'Weights for BCs and eq:{lamB},{lamE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75dadc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Adam Optimizer\n",
    "@jit\n",
    "def update(params, \n",
    "           lambdas,\n",
    "           opt_state_w, \n",
    "           data: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray],\n",
    "           idx_res,\n",
    "           epoch):\n",
    "    X_BCs_U, X_BCs_L,X_BCs_R,X_BCs_Lf, X_res  = data\n",
    "    X_res_subset=X_res[idx_res]\n",
    "    eq1                 = PDE(params)\n",
    "    #compute Errors\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res_subset)[:,None]\n",
    "    r_j=jnp.abs(e_helmholtz)\n",
    "    #Update lambdas\n",
    "    lam_res             = (gamma)*lambdas['Res'][idx_res] + lr_lambdas_0*(r_j/jnp.max(r_j))\n",
    "    lambdas['Res']      = lambdas['Res'].at[idx_res].set(lam_res)\n",
    "    def loss_fn(params):\n",
    "        X_res_subset=X_res[idx_res]\n",
    "        # Call Functions\n",
    "        eq1                 = PDE(params)\n",
    "        u_fx                = u_model(params)\n",
    "        # Residuals prediction\n",
    "        e_helmholtz         = vmap(eq1, (0))(X_res_subset)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_up          = X_BCs_U[:, :2]\n",
    "        u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_low        = X_BCs_L[:, :2]\n",
    "        u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_r        = X_BCs_R[:, :2]\n",
    "        u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "        # BCs prediction \n",
    "        tx_lft        = X_BCs_Lf[:, :2]\n",
    "        u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "        #Add \n",
    "        # Loss Weights\n",
    "        lamE_it=use_RBA*lambdas['Res'][idx_res]+(1-use_RBA)+lam_min    \n",
    "        lamB_it=1\n",
    "        #Loss Equation\n",
    "        loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "        #Total Loss\n",
    "        loss                = (        \n",
    "                              + loss_helmholtz\n",
    "                              )\n",
    "        return loss, (\n",
    "                     (loss,\n",
    "                      Error_fn(e_helmholtz,0.0)),\n",
    "                     lambdas\n",
    "        )\n",
    "\n",
    "    grad_fn = value_and_grad(loss_fn, has_aux=True)\n",
    "    #Update weights\n",
    "    (_, aux_vals),all_grads=grad_fn(params)\n",
    "    losses,lambdas         =aux_vals\n",
    "    grad_w                 =all_grads\n",
    "    updates, opt_state_w = optimizer_w.update(grad_w, opt_state_w, params)\n",
    "    params  = optax.apply_updates(params, updates)\n",
    "    return params, lambdas, opt_state_w, losses, grad_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e569fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_errors(epoch,Errors,N_its,params,X_exact):\n",
    "    N_its=int(N_its/100)\n",
    "    errors=np.zeros((1,3))\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz   = vmap(eq1, (0))(X_res)[:,None]\n",
    "    # Prediction \n",
    "    xy, u_real          = X_exact[:, :2], X_exact[:, 2:]\n",
    "    u_pred              = vmap(u_fx, (0))(xy)[:,None]\n",
    "    u_pred              =jax.device_get(u_pred)\n",
    "    #Compute errors\n",
    "    errors[0,0]=relative_error2(u_pred,u_real)\n",
    "    errors[0,1]=np.linalg.norm(u_real-u_pred,np.inf)\n",
    "    errors[0,2]=Error_fn(e_helmholtz,0.0)\n",
    "    Errors=np.vstack((Errors,errors))\n",
    "    n_errors=np.arange(len(Errors)-1)*100\n",
    "    return u_pred,e_helmholtz,errors,Errors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fb2367",
   "metadata": {},
   "source": [
    "# ADAM Training \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c57cd274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Epoch: 1, It: 1, Time: 15.15s, Learning Rate: 5.0e-03\n",
      "Epoch: 1, It: 1, Total Error: 6.817e+03, Total Loss: 4.887e+01\n",
      "Epoch: 1, It: 1, Relative L2: 1.002e+00, L Linfty: 1.098e+00\n",
      "Epoch: 1, It: 1, Loss_helmholtz: 6.817e+03\n",
      "Epoch: 1, It: 1, Lambda BCs: 0.000e+00, Lambda PDE: 3.950e-03\n",
      "--------------------------------------------------\n",
      "Epoch: 50, It: 50, Time: 0.79s, Learning Rate: 4.9e-03\n",
      "Epoch: 50, It: 50, Total Error: 4.688e+03, Total Loss: 3.236e+02\n",
      "Epoch: 50, It: 50, Relative L2: 1.724e+00, L Linfty: 2.953e+00\n",
      "Epoch: 50, It: 50, Loss_helmholtz: 4.688e+03\n",
      "Epoch: 50, It: 50, Lambda BCs: 0.000e+00, Lambda PDE: 1.536e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 100, It: 100, Time: 0.80s, Learning Rate: 4.8e-03\n",
      "Epoch: 100, It: 100, Total Error: 3.133e+03, Total Loss: 2.177e+02\n",
      "Epoch: 100, It: 100, Relative L2: 1.543e-01, L Linfty: 2.184e-01\n",
      "Epoch: 100, It: 100, Loss_helmholtz: 3.133e+03\n",
      "Epoch: 100, It: 100, Lambda BCs: 0.000e+00, Lambda PDE: 2.432e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 150, It: 150, Time: 0.80s, Learning Rate: 4.7e-03\n",
      "Epoch: 150, It: 150, Total Error: 2.350e+03, Total Loss: 1.636e+02\n",
      "Epoch: 150, It: 150, Relative L2: 4.655e-02, L Linfty: 9.498e-02\n",
      "Epoch: 150, It: 150, Loss_helmholtz: 2.350e+03\n",
      "Epoch: 150, It: 150, Lambda BCs: 0.000e+00, Lambda PDE: 2.992e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 200, It: 200, Time: 0.80s, Learning Rate: 4.7e-03\n",
      "Epoch: 200, It: 200, Total Error: 1.883e+03, Total Loss: 1.327e+02\n",
      "Epoch: 200, It: 200, Relative L2: 4.350e-02, L Linfty: 8.170e-02\n",
      "Epoch: 200, It: 200, Loss_helmholtz: 1.883e+03\n",
      "Epoch: 200, It: 200, Lambda BCs: 0.000e+00, Lambda PDE: 3.621e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 250, It: 250, Time: 0.80s, Learning Rate: 4.6e-03\n",
      "Epoch: 250, It: 250, Total Error: 1.570e+03, Total Loss: 1.109e+02\n",
      "Epoch: 250, It: 250, Relative L2: 4.592e-02, L Linfty: 8.237e-02\n",
      "Epoch: 250, It: 250, Loss_helmholtz: 1.570e+03\n",
      "Epoch: 250, It: 250, Lambda BCs: 0.000e+00, Lambda PDE: 4.274e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 300, It: 300, Time: 0.80s, Learning Rate: 4.5e-03\n",
      "Epoch: 300, It: 300, Total Error: 1.346e+03, Total Loss: 9.532e+01\n",
      "Epoch: 300, It: 300, Relative L2: 1.897e-02, L Linfty: 4.643e-02\n",
      "Epoch: 300, It: 300, Loss_helmholtz: 1.346e+03\n",
      "Epoch: 300, It: 300, Lambda BCs: 0.000e+00, Lambda PDE: 4.884e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 350, It: 350, Time: 0.80s, Learning Rate: 4.4e-03\n",
      "Epoch: 350, It: 350, Total Error: 1.178e+03, Total Loss: 8.394e+01\n",
      "Epoch: 350, It: 350, Relative L2: 4.720e-02, L Linfty: 1.019e-01\n",
      "Epoch: 350, It: 350, Loss_helmholtz: 1.178e+03\n",
      "Epoch: 350, It: 350, Lambda BCs: 0.000e+00, Lambda PDE: 5.403e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 400, It: 400, Time: 0.81s, Learning Rate: 4.3e-03\n",
      "Epoch: 400, It: 400, Total Error: 1.048e+03, Total Loss: 7.552e+01\n",
      "Epoch: 400, It: 400, Relative L2: 2.990e-02, L Linfty: 7.463e-02\n",
      "Epoch: 400, It: 400, Loss_helmholtz: 1.048e+03\n",
      "Epoch: 400, It: 400, Lambda BCs: 0.000e+00, Lambda PDE: 5.957e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 450, It: 450, Time: 0.80s, Learning Rate: 4.3e-03\n",
      "Epoch: 450, It: 450, Total Error: 9.436e+02, Total Loss: 6.820e+01\n",
      "Epoch: 450, It: 450, Relative L2: 2.529e-02, L Linfty: 5.839e-02\n",
      "Epoch: 450, It: 450, Loss_helmholtz: 9.436e+02\n",
      "Epoch: 450, It: 450, Lambda BCs: 0.000e+00, Lambda PDE: 6.391e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 500, It: 500, Time: 0.80s, Learning Rate: 4.2e-03\n",
      "Epoch: 500, It: 500, Total Error: 8.590e+02, Total Loss: 6.368e+01\n",
      "Epoch: 500, It: 500, Relative L2: 4.305e-02, L Linfty: 8.704e-02\n",
      "Epoch: 500, It: 500, Loss_helmholtz: 8.590e+02\n",
      "Epoch: 500, It: 500, Lambda BCs: 0.000e+00, Lambda PDE: 6.885e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 550, It: 550, Time: 0.80s, Learning Rate: 4.1e-03\n",
      "Epoch: 550, It: 550, Total Error: 7.875e+02, Total Loss: 5.857e+01\n",
      "Epoch: 550, It: 550, Relative L2: 1.113e-02, L Linfty: 2.225e-02\n",
      "Epoch: 550, It: 550, Loss_helmholtz: 7.875e+02\n",
      "Epoch: 550, It: 550, Lambda BCs: 0.000e+00, Lambda PDE: 7.366e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 600, It: 600, Time: 0.80s, Learning Rate: 4.0e-03\n",
      "Epoch: 600, It: 600, Total Error: 7.270e+02, Total Loss: 5.417e+01\n",
      "Epoch: 600, It: 600, Relative L2: 1.382e-02, L Linfty: 2.545e-02\n",
      "Epoch: 600, It: 600, Loss_helmholtz: 7.270e+02\n",
      "Epoch: 600, It: 600, Lambda BCs: 0.000e+00, Lambda PDE: 7.913e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 650, It: 650, Time: 0.80s, Learning Rate: 4.0e-03\n",
      "Epoch: 650, It: 650, Total Error: 6.752e+02, Total Loss: 5.042e+01\n",
      "Epoch: 650, It: 650, Relative L2: 1.320e-02, L Linfty: 2.402e-02\n",
      "Epoch: 650, It: 650, Loss_helmholtz: 6.752e+02\n",
      "Epoch: 650, It: 650, Lambda BCs: 0.000e+00, Lambda PDE: 8.279e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 700, It: 700, Time: 0.80s, Learning Rate: 3.9e-03\n",
      "Epoch: 700, It: 700, Total Error: 6.305e+02, Total Loss: 4.770e+01\n",
      "Epoch: 700, It: 700, Relative L2: 3.711e-02, L Linfty: 8.382e-02\n",
      "Epoch: 700, It: 700, Loss_helmholtz: 6.305e+02\n",
      "Epoch: 700, It: 700, Lambda BCs: 0.000e+00, Lambda PDE: 8.727e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 750, It: 750, Time: 0.80s, Learning Rate: 3.8e-03\n",
      "Epoch: 750, It: 750, Total Error: 5.911e+02, Total Loss: 4.473e+01\n",
      "Epoch: 750, It: 750, Relative L2: 4.196e-03, L Linfty: 9.570e-03\n",
      "Epoch: 750, It: 750, Loss_helmholtz: 5.911e+02\n",
      "Epoch: 750, It: 750, Lambda BCs: 0.000e+00, Lambda PDE: 9.068e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 800, It: 800, Time: 0.80s, Learning Rate: 3.8e-03\n",
      "Epoch: 800, It: 800, Total Error: 5.564e+02, Total Loss: 4.218e+01\n",
      "Epoch: 800, It: 800, Relative L2: 1.322e-02, L Linfty: 2.392e-02\n",
      "Epoch: 800, It: 800, Loss_helmholtz: 5.564e+02\n",
      "Epoch: 800, It: 800, Lambda BCs: 0.000e+00, Lambda PDE: 9.365e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 850, It: 850, Time: 0.80s, Learning Rate: 3.7e-03\n",
      "Epoch: 850, It: 850, Total Error: 5.255e+02, Total Loss: 3.992e+01\n",
      "Epoch: 850, It: 850, Relative L2: 1.246e-02, L Linfty: 2.393e-02\n",
      "Epoch: 850, It: 850, Loss_helmholtz: 5.255e+02\n",
      "Epoch: 850, It: 850, Lambda BCs: 0.000e+00, Lambda PDE: 9.761e-01\n",
      "--------------------------------------------------\n",
      "Epoch: 900, It: 900, Time: 0.80s, Learning Rate: 3.6e-03\n",
      "Epoch: 900, It: 900, Total Error: 4.981e+02, Total Loss: 3.821e+01\n",
      "Epoch: 900, It: 900, Relative L2: 3.628e-02, L Linfty: 7.989e-02\n",
      "Epoch: 900, It: 900, Loss_helmholtz: 4.981e+02\n",
      "Epoch: 900, It: 900, Lambda BCs: 0.000e+00, Lambda PDE: 1.012e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 950, It: 950, Time: 0.80s, Learning Rate: 3.6e-03\n",
      "Epoch: 950, It: 950, Total Error: 4.735e+02, Total Loss: 3.675e+01\n",
      "Epoch: 950, It: 950, Relative L2: 3.413e-02, L Linfty: 7.583e-02\n",
      "Epoch: 950, It: 950, Loss_helmholtz: 4.735e+02\n",
      "Epoch: 950, It: 950, Lambda BCs: 0.000e+00, Lambda PDE: 1.048e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1000, It: 1000, Time: 0.80s, Learning Rate: 3.5e-03\n",
      "Epoch: 1000, It: 1000, Total Error: 4.510e+02, Total Loss: 3.514e+01\n",
      "Epoch: 1000, It: 1000, Relative L2: 1.424e-02, L Linfty: 2.750e-02\n",
      "Epoch: 1000, It: 1000, Loss_helmholtz: 4.510e+02\n",
      "Epoch: 1000, It: 1000, Lambda BCs: 0.000e+00, Lambda PDE: 1.079e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1050, It: 1050, Time: 0.80s, Learning Rate: 3.4e-03\n",
      "Epoch: 1050, It: 1050, Total Error: 4.305e+02, Total Loss: 3.356e+01\n",
      "Epoch: 1050, It: 1050, Relative L2: 5.802e-03, L Linfty: 1.357e-02\n",
      "Epoch: 1050, It: 1050, Loss_helmholtz: 4.305e+02\n",
      "Epoch: 1050, It: 1050, Lambda BCs: 0.000e+00, Lambda PDE: 1.117e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1100, It: 1100, Time: 0.80s, Learning Rate: 3.4e-03\n",
      "Epoch: 1100, It: 1100, Total Error: 4.118e+02, Total Loss: 3.221e+01\n",
      "Epoch: 1100, It: 1100, Relative L2: 1.966e-02, L Linfty: 3.820e-02\n",
      "Epoch: 1100, It: 1100, Loss_helmholtz: 4.118e+02\n",
      "Epoch: 1100, It: 1100, Lambda BCs: 0.000e+00, Lambda PDE: 1.145e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1150, It: 1150, Time: 0.80s, Learning Rate: 3.3e-03\n",
      "Epoch: 1150, It: 1150, Total Error: 3.947e+02, Total Loss: 3.088e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 1200, It: 1200, Time: 0.80s, Learning Rate: 3.3e-03\n",
      "Epoch: 1200, It: 1200, Total Error: 3.789e+02, Total Loss: 2.974e+01\n",
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      "Epoch: 1200, It: 1200, Loss_helmholtz: 3.789e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1250, It: 1250, Time: 0.80s, Learning Rate: 3.2e-03\n",
      "Epoch: 1250, It: 1250, Total Error: 3.644e+02, Total Loss: 2.860e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 1300, It: 1300, Time: 0.81s, Learning Rate: 3.1e-03\n",
      "Epoch: 1300, It: 1300, Total Error: 3.510e+02, Total Loss: 2.777e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 1350, It: 1350, Time: 0.80s, Learning Rate: 3.1e-03\n",
      "Epoch: 1350, It: 1350, Total Error: 3.384e+02, Total Loss: 2.680e+01\n",
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      "Epoch: 1350, It: 1350, Loss_helmholtz: 3.384e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1400, It: 1400, Time: 0.80s, Learning Rate: 3.0e-03\n",
      "Epoch: 1400, It: 1400, Total Error: 3.268e+02, Total Loss: 2.588e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 1450, It: 1450, Time: 0.80s, Learning Rate: 3.0e-03\n",
      "Epoch: 1450, It: 1450, Total Error: 3.159e+02, Total Loss: 2.505e+01\n",
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      "Epoch: 1450, It: 1450, Loss_helmholtz: 3.159e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1500, It: 1500, Time: 0.80s, Learning Rate: 2.9e-03\n",
      "Epoch: 1500, It: 1500, Total Error: 3.059e+02, Total Loss: 2.476e+01\n",
      "Epoch: 1500, It: 1500, Relative L2: 3.134e-02, L Linfty: 7.984e-02\n",
      "Epoch: 1500, It: 1500, Loss_helmholtz: 3.059e+02\n",
      "Epoch: 1500, It: 1500, Lambda BCs: 0.000e+00, Lambda PDE: 1.255e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1550, It: 1550, Time: 0.80s, Learning Rate: 2.9e-03\n",
      "Epoch: 1550, It: 1550, Total Error: 2.963e+02, Total Loss: 2.399e+01\n",
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      "Epoch: 1550, It: 1550, Loss_helmholtz: 2.963e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1600, It: 1600, Time: 0.80s, Learning Rate: 2.8e-03\n",
      "Epoch: 1600, It: 1600, Total Error: 2.874e+02, Total Loss: 2.331e+01\n",
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      "Epoch: 1600, It: 1600, Loss_helmholtz: 2.874e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1650, It: 1650, Time: 0.80s, Learning Rate: 2.8e-03\n",
      "Epoch: 1650, It: 1650, Total Error: 2.790e+02, Total Loss: 2.277e+01\n",
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      "Epoch: 1650, It: 1650, Loss_helmholtz: 2.790e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1700, It: 1700, Time: 0.80s, Learning Rate: 2.7e-03\n",
      "Epoch: 1700, It: 1700, Total Error: 2.710e+02, Total Loss: 2.213e+01\n",
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      "Epoch: 1700, It: 1700, Loss_helmholtz: 2.710e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1750, It: 1750, Time: 0.80s, Learning Rate: 2.7e-03\n",
      "Epoch: 1750, It: 1750, Total Error: 2.635e+02, Total Loss: 2.151e+01\n",
      "Epoch: 1750, It: 1750, Relative L2: 1.510e-03, L Linfty: 3.809e-03\n",
      "Epoch: 1750, It: 1750, Loss_helmholtz: 2.635e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 1800, It: 1800, Time: 0.80s, Learning Rate: 2.6e-03\n",
      "Epoch: 1800, It: 1800, Total Error: 2.564e+02, Total Loss: 2.097e+01\n",
      "Epoch: 1800, It: 1800, Relative L2: 8.670e-03, L Linfty: 2.300e-02\n",
      "Epoch: 1800, It: 1800, Loss_helmholtz: 2.564e+02\n",
      "Epoch: 1800, It: 1800, Lambda BCs: 0.000e+00, Lambda PDE: 1.281e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1850, It: 1850, Time: 0.80s, Learning Rate: 2.6e-03\n",
      "Epoch: 1850, It: 1850, Total Error: 2.496e+02, Total Loss: 2.042e+01\n",
      "Epoch: 1850, It: 1850, Relative L2: 9.396e-04, L Linfty: 2.574e-03\n",
      "Epoch: 1850, It: 1850, Loss_helmholtz: 2.496e+02\n",
      "Epoch: 1850, It: 1850, Lambda BCs: 0.000e+00, Lambda PDE: 1.284e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1900, It: 1900, Time: 0.80s, Learning Rate: 2.5e-03\n",
      "Epoch: 1900, It: 1900, Total Error: 2.432e+02, Total Loss: 1.990e+01\n",
      "Epoch: 1900, It: 1900, Relative L2: 6.888e-04, L Linfty: 1.766e-03\n",
      "Epoch: 1900, It: 1900, Loss_helmholtz: 2.432e+02\n",
      "Epoch: 1900, It: 1900, Lambda BCs: 0.000e+00, Lambda PDE: 1.287e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 1950, It: 1950, Time: 0.81s, Learning Rate: 2.5e-03\n",
      "Epoch: 1950, It: 1950, Total Error: 2.371e+02, Total Loss: 1.940e+01\n",
      "Epoch: 1950, It: 1950, Relative L2: 6.481e-04, L Linfty: 1.647e-03\n",
      "Epoch: 1950, It: 1950, Loss_helmholtz: 2.371e+02\n",
      "Epoch: 1950, It: 1950, Lambda BCs: 0.000e+00, Lambda PDE: 1.292e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2000, It: 2000, Time: 0.80s, Learning Rate: 2.4e-03\n",
      "Epoch: 2000, It: 2000, Total Error: 2.314e+02, Total Loss: 1.893e+01\n",
      "Epoch: 2000, It: 2000, Relative L2: 6.388e-04, L Linfty: 1.641e-03\n",
      "Epoch: 2000, It: 2000, Loss_helmholtz: 2.314e+02\n",
      "Epoch: 2000, It: 2000, Lambda BCs: 0.000e+00, Lambda PDE: 1.298e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2050, It: 2050, Time: 0.80s, Learning Rate: 2.4e-03\n",
      "Epoch: 2050, It: 2050, Total Error: 2.258e+02, Total Loss: 1.848e+01\n",
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      "Epoch: 2050, It: 2050, Loss_helmholtz: 2.258e+02\n",
      "Epoch: 2050, It: 2050, Lambda BCs: 0.000e+00, Lambda PDE: 1.304e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2100, It: 2100, Time: 0.80s, Learning Rate: 2.4e-03\n",
      "Epoch: 2100, It: 2100, Total Error: 2.206e+02, Total Loss: 1.805e+01\n",
      "Epoch: 2100, It: 2100, Relative L2: 6.501e-04, L Linfty: 1.541e-03\n",
      "Epoch: 2100, It: 2100, Loss_helmholtz: 2.206e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 2150, It: 2150, Time: 0.80s, Learning Rate: 2.3e-03\n",
      "Epoch: 2150, It: 2150, Total Error: 2.156e+02, Total Loss: 1.764e+01\n",
      "Epoch: 2150, It: 2150, Relative L2: 7.386e-04, L Linfty: 1.754e-03\n",
      "Epoch: 2150, It: 2150, Loss_helmholtz: 2.156e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 2200, It: 2200, Time: 0.80s, Learning Rate: 2.3e-03\n",
      "Epoch: 2200, It: 2200, Total Error: 2.108e+02, Total Loss: 1.725e+01\n",
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      "Epoch: 2200, It: 2200, Loss_helmholtz: 2.108e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 2250, It: 2250, Time: 0.80s, Learning Rate: 2.2e-03\n",
      "Epoch: 2250, It: 2250, Total Error: 2.062e+02, Total Loss: 1.687e+01\n",
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      "Epoch: 2250, It: 2250, Loss_helmholtz: 2.062e+02\n",
      "Epoch: 2250, It: 2250, Lambda BCs: 0.000e+00, Lambda PDE: 1.332e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2300, It: 2300, Time: 0.80s, Learning Rate: 2.2e-03\n",
      "Epoch: 2300, It: 2300, Total Error: 2.018e+02, Total Loss: 1.651e+01\n",
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      "Epoch: 2300, It: 2300, Loss_helmholtz: 2.018e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 2350, It: 2350, Time: 0.80s, Learning Rate: 2.2e-03\n",
      "Epoch: 2350, It: 2350, Total Error: 1.976e+02, Total Loss: 1.617e+01\n",
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      "Epoch: 2350, It: 2350, Loss_helmholtz: 1.976e+02\n",
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      "--------------------------------------------------\n",
      "Epoch: 2400, It: 2400, Time: 0.80s, Learning Rate: 2.1e-03\n",
      "Epoch: 2400, It: 2400, Total Error: 1.936e+02, Total Loss: 1.584e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 2450, It: 2450, Time: 0.80s, Learning Rate: 2.1e-03\n",
      "Epoch: 2450, It: 2450, Total Error: 1.897e+02, Total Loss: 1.552e+01\n",
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      "Epoch: 2450, It: 2450, Loss_helmholtz: 1.897e+02\n",
      "Epoch: 2450, It: 2450, Lambda BCs: 0.000e+00, Lambda PDE: 1.363e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2500, It: 2500, Time: 0.80s, Learning Rate: 2.0e-03\n",
      "Epoch: 2500, It: 2500, Total Error: 1.860e+02, Total Loss: 1.522e+01\n",
      "Epoch: 2500, It: 2500, Relative L2: 4.980e-03, L Linfty: 8.668e-03\n",
      "Epoch: 2500, It: 2500, Loss_helmholtz: 1.860e+02\n",
      "Epoch: 2500, It: 2500, Lambda BCs: 0.000e+00, Lambda PDE: 1.363e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2550, It: 2550, Time: 0.80s, Learning Rate: 2.0e-03\n",
      "Epoch: 2550, It: 2550, Total Error: 1.824e+02, Total Loss: 1.493e+01\n",
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      "Epoch: 2550, It: 2550, Loss_helmholtz: 1.824e+02\n",
      "Epoch: 2550, It: 2550, Lambda BCs: 0.000e+00, Lambda PDE: 1.378e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2600, It: 2600, Time: 0.80s, Learning Rate: 2.0e-03\n",
      "Epoch: 2600, It: 2600, Total Error: 1.790e+02, Total Loss: 1.466e+01\n",
      "Epoch: 2600, It: 2600, Relative L2: 8.723e-03, L Linfty: 2.490e-02\n",
      "Epoch: 2600, It: 2600, Loss_helmholtz: 1.790e+02\n",
      "Epoch: 2600, It: 2600, Lambda BCs: 0.000e+00, Lambda PDE: 1.384e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2650, It: 2650, Time: 0.80s, Learning Rate: 1.9e-03\n",
      "Epoch: 2650, It: 2650, Total Error: 1.757e+02, Total Loss: 1.439e+01\n",
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      "Epoch: 2650, It: 2650, Loss_helmholtz: 1.757e+02\n",
      "Epoch: 2650, It: 2650, Lambda BCs: 0.000e+00, Lambda PDE: 1.386e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2700, It: 2700, Time: 0.80s, Learning Rate: 1.9e-03\n",
      "Epoch: 2700, It: 2700, Total Error: 1.725e+02, Total Loss: 1.418e+01\n",
      "Epoch: 2700, It: 2700, Relative L2: 1.973e-02, L Linfty: 4.392e-02\n",
      "Epoch: 2700, It: 2700, Loss_helmholtz: 1.725e+02\n",
      "Epoch: 2700, It: 2700, Lambda BCs: 0.000e+00, Lambda PDE: 1.398e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2750, It: 2750, Time: 0.80s, Learning Rate: 1.9e-03\n",
      "Epoch: 2750, It: 2750, Total Error: 1.694e+02, Total Loss: 1.393e+01\n",
      "Epoch: 2750, It: 2750, Relative L2: 4.443e-03, L Linfty: 1.140e-02\n",
      "Epoch: 2750, It: 2750, Loss_helmholtz: 1.694e+02\n",
      "Epoch: 2750, It: 2750, Lambda BCs: 0.000e+00, Lambda PDE: 1.417e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2800, It: 2800, Time: 0.80s, Learning Rate: 1.8e-03\n",
      "Epoch: 2800, It: 2800, Total Error: 1.665e+02, Total Loss: 1.369e+01\n",
      "Epoch: 2800, It: 2800, Relative L2: 5.074e-03, L Linfty: 1.346e-02\n",
      "Epoch: 2800, It: 2800, Loss_helmholtz: 1.665e+02\n",
      "Epoch: 2800, It: 2800, Lambda BCs: 0.000e+00, Lambda PDE: 1.421e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2850, It: 2850, Time: 0.80s, Learning Rate: 1.8e-03\n",
      "Epoch: 2850, It: 2850, Total Error: 1.636e+02, Total Loss: 1.347e+01\n",
      "Epoch: 2850, It: 2850, Relative L2: 1.075e-02, L Linfty: 2.842e-02\n",
      "Epoch: 2850, It: 2850, Loss_helmholtz: 1.636e+02\n",
      "Epoch: 2850, It: 2850, Lambda BCs: 0.000e+00, Lambda PDE: 1.421e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2900, It: 2900, Time: 0.80s, Learning Rate: 1.8e-03\n",
      "Epoch: 2900, It: 2900, Total Error: 1.608e+02, Total Loss: 1.324e+01\n",
      "Epoch: 2900, It: 2900, Relative L2: 1.050e-03, L Linfty: 3.084e-03\n",
      "Epoch: 2900, It: 2900, Loss_helmholtz: 1.608e+02\n",
      "Epoch: 2900, It: 2900, Lambda BCs: 0.000e+00, Lambda PDE: 1.421e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 2950, It: 2950, Time: 0.80s, Learning Rate: 1.7e-03\n",
      "Epoch: 2950, It: 2950, Total Error: 1.582e+02, Total Loss: 1.305e+01\n",
      "Epoch: 2950, It: 2950, Relative L2: 4.702e-03, L Linfty: 8.097e-03\n",
      "Epoch: 2950, It: 2950, Loss_helmholtz: 1.582e+02\n",
      "Epoch: 2950, It: 2950, Lambda BCs: 0.000e+00, Lambda PDE: 1.426e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3000, It: 3000, Time: 0.80s, Learning Rate: 1.7e-03\n",
      "Epoch: 3000, It: 3000, Total Error: 1.556e+02, Total Loss: 1.283e+01\n",
      "Epoch: 3000, It: 3000, Relative L2: 2.043e-03, L Linfty: 5.526e-03\n",
      "Epoch: 3000, It: 3000, Loss_helmholtz: 1.556e+02\n",
      "Epoch: 3000, It: 3000, Lambda BCs: 0.000e+00, Lambda PDE: 1.424e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3050, It: 3050, Time: 0.80s, Learning Rate: 1.7e-03\n",
      "Epoch: 3050, It: 3050, Total Error: 1.531e+02, Total Loss: 1.263e+01\n",
      "Epoch: 3050, It: 3050, Relative L2: 5.509e-04, L Linfty: 1.514e-03\n",
      "Epoch: 3050, It: 3050, Loss_helmholtz: 1.531e+02\n",
      "Epoch: 3050, It: 3050, Lambda BCs: 0.000e+00, Lambda PDE: 1.434e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3100, It: 3100, Time: 0.80s, Learning Rate: 1.7e-03\n",
      "Epoch: 3100, It: 3100, Total Error: 1.506e+02, Total Loss: 1.243e+01\n",
      "Epoch: 3100, It: 3100, Relative L2: 5.511e-04, L Linfty: 1.583e-03\n",
      "Epoch: 3100, It: 3100, Loss_helmholtz: 1.506e+02\n",
      "Epoch: 3100, It: 3100, Lambda BCs: 0.000e+00, Lambda PDE: 1.443e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3150, It: 3150, Time: 0.80s, Learning Rate: 1.6e-03\n",
      "Epoch: 3150, It: 3150, Total Error: 1.483e+02, Total Loss: 1.223e+01\n",
      "Epoch: 3150, It: 3150, Relative L2: 5.764e-04, L Linfty: 1.598e-03\n",
      "Epoch: 3150, It: 3150, Loss_helmholtz: 1.483e+02\n",
      "Epoch: 3150, It: 3150, Lambda BCs: 0.000e+00, Lambda PDE: 1.451e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3200, It: 3200, Time: 0.80s, Learning Rate: 1.6e-03\n",
      "Epoch: 3200, It: 3200, Total Error: 1.460e+02, Total Loss: 1.204e+01\n",
      "Epoch: 3200, It: 3200, Relative L2: 5.443e-04, L Linfty: 1.636e-03\n",
      "Epoch: 3200, It: 3200, Loss_helmholtz: 1.460e+02\n",
      "Epoch: 3200, It: 3200, Lambda BCs: 0.000e+00, Lambda PDE: 1.459e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 3250, It: 3250, Time: 0.80s, Learning Rate: 1.6e-03\n",
      "Epoch: 3250, It: 3250, Total Error: 1.438e+02, Total Loss: 1.186e+01\n",
      "Epoch: 3250, It: 3250, Relative L2: 5.306e-04, L Linfty: 1.586e-03\n",
      "Epoch: 3250, It: 3250, Loss_helmholtz: 1.438e+02\n",
      "Epoch: 3250, It: 3250, Lambda BCs: 0.000e+00, Lambda PDE: 1.466e+00\n",
      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 4050, It: 4050, Time: 0.80s, Learning Rate: 1.2e-03\n",
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      "--------------------------------------------------\n",
      "Epoch: 4100, It: 4100, Time: 0.80s, Learning Rate: 1.2e-03\n",
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      "--------------------------------------------------\n",
      "Epoch: 4150, It: 4150, Time: 0.80s, Learning Rate: 1.1e-03\n",
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      "--------------------------------------------------\n",
      "Epoch: 4200, It: 4200, Time: 0.80s, Learning Rate: 1.1e-03\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 4300, It: 4300, Time: 0.80s, Learning Rate: 1.1e-03\n",
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      "--------------------------------------------------\n",
      "Epoch: 4350, It: 4350, Time: 0.80s, Learning Rate: 1.1e-03\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 4450, It: 4450, Time: 0.82s, Learning Rate: 1.0e-03\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 5100, It: 5100, Time: 0.80s, Learning Rate: 8.1e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 5450, It: 5450, Time: 0.80s, Learning Rate: 7.2e-04\n",
      "Epoch: 5450, It: 5450, Total Error: 8.629e+01, Total Loss: 7.163e+00\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 6950, It: 6950, Time: 0.80s, Learning Rate: 4.2e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 7050, It: 7050, Time: 0.81s, Learning Rate: 4.0e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 7150, It: 7150, Time: 0.80s, Learning Rate: 3.9e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 7600, It: 7600, Time: 0.80s, Learning Rate: 3.3e-04\n",
      "Epoch: 7600, It: 7600, Total Error: 6.204e+01, Total Loss: 5.155e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 7650, It: 7650, Time: 0.80s, Learning Rate: 3.3e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 7750, It: 7750, Time: 0.80s, Learning Rate: 3.2e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 7800, It: 7800, Time: 0.80s, Learning Rate: 3.1e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 7850, It: 7850, Time: 0.80s, Learning Rate: 3.0e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 7950, It: 7950, Time: 0.80s, Learning Rate: 2.9e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8000, It: 8000, Time: 0.80s, Learning Rate: 2.9e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8050, It: 8050, Time: 0.81s, Learning Rate: 2.8e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8100, It: 8100, Time: 0.80s, Learning Rate: 2.8e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8150, It: 8150, Time: 0.80s, Learning Rate: 2.7e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8200, It: 8200, Time: 0.80s, Learning Rate: 2.7e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8250, It: 8250, Time: 0.80s, Learning Rate: 2.6e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8300, It: 8300, Time: 0.80s, Learning Rate: 2.6e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8350, It: 8350, Time: 0.80s, Learning Rate: 2.5e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8400, It: 8400, Time: 0.80s, Learning Rate: 2.5e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8450, It: 8450, Time: 0.80s, Learning Rate: 2.5e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8500, It: 8500, Time: 0.80s, Learning Rate: 2.4e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8550, It: 8550, Time: 0.80s, Learning Rate: 2.4e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8600, It: 8600, Time: 0.80s, Learning Rate: 2.3e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8650, It: 8650, Time: 0.80s, Learning Rate: 2.3e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8700, It: 8700, Time: 0.81s, Learning Rate: 2.2e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8750, It: 8750, Time: 0.81s, Learning Rate: 2.2e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8800, It: 8800, Time: 0.80s, Learning Rate: 2.2e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 8850, It: 8850, Time: 0.80s, Learning Rate: 2.1e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 8950, It: 8950, Time: 0.80s, Learning Rate: 2.1e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9000, It: 9000, Time: 0.80s, Learning Rate: 2.0e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9050, It: 9050, Time: 0.80s, Learning Rate: 2.0e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9100, It: 9100, Time: 0.80s, Learning Rate: 1.9e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9150, It: 9150, Time: 0.80s, Learning Rate: 1.9e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9200, It: 9200, Time: 0.80s, Learning Rate: 1.9e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9250, It: 9250, Time: 0.82s, Learning Rate: 1.8e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9300, It: 9300, Time: 0.80s, Learning Rate: 1.8e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9350, It: 9350, Time: 0.80s, Learning Rate: 1.8e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9400, It: 9400, Time: 0.80s, Learning Rate: 1.7e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9450, It: 9450, Time: 0.80s, Learning Rate: 1.7e-04\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 9700, It: 9700, Time: 0.80s, Learning Rate: 1.6e-04\n",
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      "--------------------------------------------------\n",
      "Epoch: 9750, It: 9750, Time: 0.80s, Learning Rate: 1.5e-04\n",
      "Epoch: 9750, It: 9750, Total Error: 4.843e+01, Total Loss: 4.026e+00\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 11900, It: 11900, Time: 0.80s, Learning Rate: 7.2e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 11950, It: 11950, Time: 0.80s, Learning Rate: 7.0e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12000, It: 12000, Time: 0.80s, Learning Rate: 6.9e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12050, It: 12050, Time: 0.80s, Learning Rate: 6.8e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12100, It: 12100, Time: 0.80s, Learning Rate: 6.7e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12150, It: 12150, Time: 0.80s, Learning Rate: 6.6e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12200, It: 12200, Time: 0.80s, Learning Rate: 6.4e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12250, It: 12250, Time: 0.81s, Learning Rate: 6.3e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12300, It: 12300, Time: 0.80s, Learning Rate: 6.2e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12350, It: 12350, Time: 0.80s, Learning Rate: 6.1e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12400, It: 12400, Time: 0.80s, Learning Rate: 6.0e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12450, It: 12450, Time: 0.80s, Learning Rate: 5.9e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12500, It: 12500, Time: 0.81s, Learning Rate: 5.8e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 12600, It: 12600, Time: 0.80s, Learning Rate: 5.6e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12650, It: 12650, Time: 0.80s, Learning Rate: 5.5e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12700, It: 12700, Time: 0.80s, Learning Rate: 5.4e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12750, It: 12750, Time: 0.80s, Learning Rate: 5.3e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12800, It: 12800, Time: 0.80s, Learning Rate: 5.2e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12850, It: 12850, Time: 0.81s, Learning Rate: 5.1e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 12900, It: 12900, Time: 0.81s, Learning Rate: 5.0e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 13000, It: 13000, Time: 0.80s, Learning Rate: 4.8e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 13050, It: 13050, Time: 0.80s, Learning Rate: 4.8e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 13100, It: 13100, Time: 0.80s, Learning Rate: 4.7e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 13150, It: 13150, Time: 0.81s, Learning Rate: 4.6e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 13250, It: 13250, Time: 0.80s, Learning Rate: 4.4e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 13400, It: 13400, Time: 0.80s, Learning Rate: 4.2e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 13550, It: 13550, Time: 0.80s, Learning Rate: 4.0e-05\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
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      "--------------------------------------------------\n",
      "Epoch: 14000, It: 14000, Time: 0.80s, Learning Rate: 3.4e-05\n",
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      "--------------------------------------------------\n",
      "Epoch: 14050, It: 14050, Time: 0.80s, Learning Rate: 3.3e-05\n",
      "Epoch: 14050, It: 14050, Total Error: 3.366e+01, Total Loss: 2.799e+00\n",
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      "Epoch: 14050, It: 14050, Loss_helmholtz: 3.366e+01\n",
      "Epoch: 14050, It: 14050, Lambda BCs: 0.000e+00, Lambda PDE: 1.803e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 14100, It: 14100, Time: 0.80s, Learning Rate: 3.3e-05\n",
      "Epoch: 14100, It: 14100, Total Error: 3.354e+01, Total Loss: 2.789e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 14150, It: 14150, Time: 0.81s, Learning Rate: 3.2e-05\n",
      "Epoch: 14150, It: 14150, Total Error: 3.342e+01, Total Loss: 2.780e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 14200, It: 14200, Time: 0.81s, Learning Rate: 3.2e-05\n",
      "Epoch: 14200, It: 14200, Total Error: 3.331e+01, Total Loss: 2.770e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 14250, It: 14250, Time: 0.82s, Learning Rate: 3.1e-05\n",
      "Epoch: 14250, It: 14250, Total Error: 3.319e+01, Total Loss: 2.760e+00\n",
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      "Epoch: 14250, It: 14250, Loss_helmholtz: 3.319e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14300, It: 14300, Time: 0.80s, Learning Rate: 3.0e-05\n",
      "Epoch: 14300, It: 14300, Total Error: 3.307e+01, Total Loss: 2.751e+00\n",
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      "Epoch: 14300, It: 14300, Loss_helmholtz: 3.307e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14350, It: 14350, Time: 0.80s, Learning Rate: 3.0e-05\n",
      "Epoch: 14350, It: 14350, Total Error: 3.296e+01, Total Loss: 2.741e+00\n",
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      "Epoch: 14350, It: 14350, Loss_helmholtz: 3.296e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14400, It: 14400, Time: 0.80s, Learning Rate: 2.9e-05\n",
      "Epoch: 14400, It: 14400, Total Error: 3.284e+01, Total Loss: 2.732e+00\n",
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      "Epoch: 14400, It: 14400, Loss_helmholtz: 3.284e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14450, It: 14450, Time: 0.80s, Learning Rate: 2.9e-05\n",
      "Epoch: 14450, It: 14450, Total Error: 3.273e+01, Total Loss: 2.722e+00\n",
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      "Epoch: 14450, It: 14450, Loss_helmholtz: 3.273e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14500, It: 14500, Time: 0.80s, Learning Rate: 2.8e-05\n",
      "Epoch: 14500, It: 14500, Total Error: 3.262e+01, Total Loss: 2.713e+00\n",
      "Epoch: 14500, It: 14500, Relative L2: 4.399e-04, L Linfty: 1.402e-03\n",
      "Epoch: 14500, It: 14500, Loss_helmholtz: 3.262e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14550, It: 14550, Time: 0.80s, Learning Rate: 2.8e-05\n",
      "Epoch: 14550, It: 14550, Total Error: 3.251e+01, Total Loss: 2.704e+00\n",
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      "Epoch: 14550, It: 14550, Loss_helmholtz: 3.251e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14600, It: 14600, Time: 0.80s, Learning Rate: 2.7e-05\n",
      "Epoch: 14600, It: 14600, Total Error: 3.240e+01, Total Loss: 2.694e+00\n",
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      "Epoch: 14600, It: 14600, Loss_helmholtz: 3.240e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14650, It: 14650, Time: 0.80s, Learning Rate: 2.7e-05\n",
      "Epoch: 14650, It: 14650, Total Error: 3.229e+01, Total Loss: 2.685e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 14700, It: 14700, Time: 0.80s, Learning Rate: 2.6e-05\n",
      "Epoch: 14700, It: 14700, Total Error: 3.218e+01, Total Loss: 2.676e+00\n",
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      "Epoch: 14700, It: 14700, Loss_helmholtz: 3.218e+01\n",
      "Epoch: 14700, It: 14700, Lambda BCs: 0.000e+00, Lambda PDE: 1.851e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 14750, It: 14750, Time: 0.80s, Learning Rate: 2.6e-05\n",
      "Epoch: 14750, It: 14750, Total Error: 3.207e+01, Total Loss: 2.667e+00\n",
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      "Epoch: 14750, It: 14750, Loss_helmholtz: 3.207e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14800, It: 14800, Time: 0.80s, Learning Rate: 2.5e-05\n",
      "Epoch: 14800, It: 14800, Total Error: 3.196e+01, Total Loss: 2.658e+00\n",
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      "Epoch: 14800, It: 14800, Loss_helmholtz: 3.196e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14850, It: 14850, Time: 0.80s, Learning Rate: 2.5e-05\n",
      "Epoch: 14850, It: 14850, Total Error: 3.185e+01, Total Loss: 2.649e+00\n",
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      "Epoch: 14850, It: 14850, Loss_helmholtz: 3.185e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14900, It: 14900, Time: 0.80s, Learning Rate: 2.5e-05\n",
      "Epoch: 14900, It: 14900, Total Error: 3.175e+01, Total Loss: 2.640e+00\n",
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      "Epoch: 14900, It: 14900, Loss_helmholtz: 3.175e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 14950, It: 14950, Time: 0.81s, Learning Rate: 2.4e-05\n",
      "Epoch: 14950, It: 14950, Total Error: 3.164e+01, Total Loss: 2.631e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 15000, It: 15000, Time: 0.81s, Learning Rate: 2.4e-05\n",
      "Epoch: 15000, It: 15000, Total Error: 3.154e+01, Total Loss: 2.623e+00\n",
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      "Epoch: 15000, It: 15000, Loss_helmholtz: 3.154e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15050, It: 15050, Time: 0.80s, Learning Rate: 2.3e-05\n",
      "Epoch: 15050, It: 15050, Total Error: 3.143e+01, Total Loss: 2.614e+00\n",
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      "Epoch: 15050, It: 15050, Loss_helmholtz: 3.143e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15100, It: 15100, Time: 0.80s, Learning Rate: 2.3e-05\n",
      "Epoch: 15100, It: 15100, Total Error: 3.133e+01, Total Loss: 2.605e+00\n",
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      "Epoch: 15100, It: 15100, Loss_helmholtz: 3.133e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15150, It: 15150, Time: 0.80s, Learning Rate: 2.3e-05\n",
      "Epoch: 15150, It: 15150, Total Error: 3.122e+01, Total Loss: 2.597e+00\n",
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      "Epoch: 15150, It: 15150, Loss_helmholtz: 3.122e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15200, It: 15200, Time: 0.80s, Learning Rate: 2.2e-05\n",
      "Epoch: 15200, It: 15200, Total Error: 3.112e+01, Total Loss: 2.588e+00\n",
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      "Epoch: 15200, It: 15200, Loss_helmholtz: 3.112e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15250, It: 15250, Time: 0.80s, Learning Rate: 2.2e-05\n",
      "Epoch: 15250, It: 15250, Total Error: 3.102e+01, Total Loss: 2.580e+00\n",
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      "Epoch: 15250, It: 15250, Loss_helmholtz: 3.102e+01\n",
      "Epoch: 15250, It: 15250, Lambda BCs: 0.000e+00, Lambda PDE: 1.870e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15300, It: 15300, Time: 0.81s, Learning Rate: 2.1e-05\n",
      "Epoch: 15300, It: 15300, Total Error: 3.092e+01, Total Loss: 2.572e+00\n",
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      "Epoch: 15300, It: 15300, Loss_helmholtz: 3.092e+01\n",
      "Epoch: 15300, It: 15300, Lambda BCs: 0.000e+00, Lambda PDE: 1.870e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15350, It: 15350, Time: 0.81s, Learning Rate: 2.1e-05\n",
      "Epoch: 15350, It: 15350, Total Error: 3.082e+01, Total Loss: 2.563e+00\n",
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      "Epoch: 15350, It: 15350, Loss_helmholtz: 3.082e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15400, It: 15400, Time: 0.80s, Learning Rate: 2.1e-05\n",
      "Epoch: 15400, It: 15400, Total Error: 3.072e+01, Total Loss: 2.555e+00\n",
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      "Epoch: 15400, It: 15400, Loss_helmholtz: 3.072e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15450, It: 15450, Time: 0.80s, Learning Rate: 2.0e-05\n",
      "Epoch: 15450, It: 15450, Total Error: 3.062e+01, Total Loss: 2.547e+00\n",
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      "Epoch: 15450, It: 15450, Loss_helmholtz: 3.062e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15500, It: 15500, Time: 0.80s, Learning Rate: 2.0e-05\n",
      "Epoch: 15500, It: 15500, Total Error: 3.052e+01, Total Loss: 2.538e+00\n",
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      "Epoch: 15500, It: 15500, Loss_helmholtz: 3.052e+01\n",
      "Epoch: 15500, It: 15500, Lambda BCs: 0.000e+00, Lambda PDE: 1.877e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15550, It: 15550, Time: 0.81s, Learning Rate: 2.0e-05\n",
      "Epoch: 15550, It: 15550, Total Error: 3.042e+01, Total Loss: 2.530e+00\n",
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      "Epoch: 15550, It: 15550, Loss_helmholtz: 3.042e+01\n",
      "Epoch: 15550, It: 15550, Lambda BCs: 0.000e+00, Lambda PDE: 1.878e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15600, It: 15600, Time: 0.81s, Learning Rate: 1.9e-05\n",
      "Epoch: 15600, It: 15600, Total Error: 3.033e+01, Total Loss: 2.522e+00\n",
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      "Epoch: 15600, It: 15600, Loss_helmholtz: 3.033e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15650, It: 15650, Time: 0.80s, Learning Rate: 1.9e-05\n",
      "Epoch: 15650, It: 15650, Total Error: 3.023e+01, Total Loss: 2.514e+00\n",
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      "Epoch: 15650, It: 15650, Loss_helmholtz: 3.023e+01\n",
      "Epoch: 15650, It: 15650, Lambda BCs: 0.000e+00, Lambda PDE: 1.875e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15700, It: 15700, Time: 0.80s, Learning Rate: 1.8e-05\n",
      "Epoch: 15700, It: 15700, Total Error: 3.013e+01, Total Loss: 2.506e+00\n",
      "Epoch: 15700, It: 15700, Relative L2: 4.199e-04, L Linfty: 1.386e-03\n",
      "Epoch: 15700, It: 15700, Loss_helmholtz: 3.013e+01\n",
      "Epoch: 15700, It: 15700, Lambda BCs: 0.000e+00, Lambda PDE: 1.877e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15750, It: 15750, Time: 0.80s, Learning Rate: 1.8e-05\n",
      "Epoch: 15750, It: 15750, Total Error: 3.004e+01, Total Loss: 2.498e+00\n",
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      "Epoch: 15750, It: 15750, Loss_helmholtz: 3.004e+01\n",
      "Epoch: 15750, It: 15750, Lambda BCs: 0.000e+00, Lambda PDE: 1.877e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 15800, It: 15800, Time: 0.80s, Learning Rate: 1.8e-05\n",
      "Epoch: 15800, It: 15800, Total Error: 2.994e+01, Total Loss: 2.490e+00\n",
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      "Epoch: 15800, It: 15800, Loss_helmholtz: 2.994e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 15850, It: 15850, Time: 0.80s, Learning Rate: 1.8e-05\n",
      "Epoch: 15850, It: 15850, Total Error: 2.985e+01, Total Loss: 2.483e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 15900, It: 15900, Time: 0.80s, Learning Rate: 1.7e-05\n",
      "Epoch: 15900, It: 15900, Total Error: 2.976e+01, Total Loss: 2.475e+00\n",
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      "--------------------------------------------------\n",
      "Epoch: 15950, It: 15950, Time: 0.80s, Learning Rate: 1.7e-05\n",
      "Epoch: 15950, It: 15950, Total Error: 2.966e+01, Total Loss: 2.467e+00\n",
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      "Epoch: 15950, It: 15950, Loss_helmholtz: 2.966e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16000, It: 16000, Time: 0.80s, Learning Rate: 1.7e-05\n",
      "Epoch: 16000, It: 16000, Total Error: 2.957e+01, Total Loss: 2.459e+00\n",
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      "Epoch: 16000, It: 16000, Loss_helmholtz: 2.957e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16050, It: 16050, Time: 0.80s, Learning Rate: 1.6e-05\n",
      "Epoch: 16050, It: 16050, Total Error: 2.948e+01, Total Loss: 2.452e+00\n",
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      "Epoch: 16050, It: 16050, Loss_helmholtz: 2.948e+01\n",
      "Epoch: 16050, It: 16050, Lambda BCs: 0.000e+00, Lambda PDE: 1.887e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16100, It: 16100, Time: 0.80s, Learning Rate: 1.6e-05\n",
      "Epoch: 16100, It: 16100, Total Error: 2.939e+01, Total Loss: 2.444e+00\n",
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      "Epoch: 16100, It: 16100, Loss_helmholtz: 2.939e+01\n",
      "Epoch: 16100, It: 16100, Lambda BCs: 0.000e+00, Lambda PDE: 1.885e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16150, It: 16150, Time: 0.81s, Learning Rate: 1.6e-05\n",
      "Epoch: 16150, It: 16150, Total Error: 2.930e+01, Total Loss: 2.437e+00\n",
      "Epoch: 16150, It: 16150, Relative L2: 4.186e-04, L Linfty: 1.390e-03\n",
      "Epoch: 16150, It: 16150, Loss_helmholtz: 2.930e+01\n",
      "Epoch: 16150, It: 16150, Lambda BCs: 0.000e+00, Lambda PDE: 1.884e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16200, It: 16200, Time: 0.80s, Learning Rate: 1.5e-05\n",
      "Epoch: 16200, It: 16200, Total Error: 2.921e+01, Total Loss: 2.429e+00\n",
      "Epoch: 16200, It: 16200, Relative L2: 4.156e-04, L Linfty: 1.365e-03\n",
      "Epoch: 16200, It: 16200, Loss_helmholtz: 2.921e+01\n",
      "Epoch: 16200, It: 16200, Lambda BCs: 0.000e+00, Lambda PDE: 1.883e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16250, It: 16250, Time: 0.81s, Learning Rate: 1.5e-05\n",
      "Epoch: 16250, It: 16250, Total Error: 2.912e+01, Total Loss: 2.422e+00\n",
      "Epoch: 16250, It: 16250, Relative L2: 4.170e-04, L Linfty: 1.405e-03\n",
      "Epoch: 16250, It: 16250, Loss_helmholtz: 2.912e+01\n",
      "Epoch: 16250, It: 16250, Lambda BCs: 0.000e+00, Lambda PDE: 1.887e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16300, It: 16300, Time: 0.80s, Learning Rate: 1.5e-05\n",
      "Epoch: 16300, It: 16300, Total Error: 2.903e+01, Total Loss: 2.414e+00\n",
      "Epoch: 16300, It: 16300, Relative L2: 4.186e-04, L Linfty: 1.468e-03\n",
      "Epoch: 16300, It: 16300, Loss_helmholtz: 2.903e+01\n",
      "Epoch: 16300, It: 16300, Lambda BCs: 0.000e+00, Lambda PDE: 1.888e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16350, It: 16350, Time: 0.80s, Learning Rate: 1.5e-05\n",
      "Epoch: 16350, It: 16350, Total Error: 2.894e+01, Total Loss: 2.407e+00\n",
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      "Epoch: 16350, It: 16350, Loss_helmholtz: 2.894e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16400, It: 16400, Time: 0.80s, Learning Rate: 1.4e-05\n",
      "Epoch: 16400, It: 16400, Total Error: 2.885e+01, Total Loss: 2.400e+00\n",
      "Epoch: 16400, It: 16400, Relative L2: 4.194e-04, L Linfty: 1.361e-03\n",
      "Epoch: 16400, It: 16400, Loss_helmholtz: 2.885e+01\n",
      "Epoch: 16400, It: 16400, Lambda BCs: 0.000e+00, Lambda PDE: 1.877e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16450, It: 16450, Time: 0.80s, Learning Rate: 1.4e-05\n",
      "Epoch: 16450, It: 16450, Total Error: 2.876e+01, Total Loss: 2.392e+00\n",
      "Epoch: 16450, It: 16450, Relative L2: 4.273e-04, L Linfty: 1.270e-03\n",
      "Epoch: 16450, It: 16450, Loss_helmholtz: 2.876e+01\n",
      "Epoch: 16450, It: 16450, Lambda BCs: 0.000e+00, Lambda PDE: 1.870e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16500, It: 16500, Time: 0.82s, Learning Rate: 1.4e-05\n",
      "Epoch: 16500, It: 16500, Total Error: 2.868e+01, Total Loss: 2.385e+00\n",
      "Epoch: 16500, It: 16500, Relative L2: 4.227e-04, L Linfty: 1.350e-03\n",
      "Epoch: 16500, It: 16500, Loss_helmholtz: 2.868e+01\n",
      "Epoch: 16500, It: 16500, Lambda BCs: 0.000e+00, Lambda PDE: 1.866e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16550, It: 16550, Time: 0.83s, Learning Rate: 1.4e-05\n",
      "Epoch: 16550, It: 16550, Total Error: 2.859e+01, Total Loss: 2.378e+00\n",
      "Epoch: 16550, It: 16550, Relative L2: 4.266e-04, L Linfty: 1.360e-03\n",
      "Epoch: 16550, It: 16550, Loss_helmholtz: 2.859e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16600, It: 16600, Time: 0.82s, Learning Rate: 1.3e-05\n",
      "Epoch: 16600, It: 16600, Total Error: 2.850e+01, Total Loss: 2.371e+00\n",
      "Epoch: 16600, It: 16600, Relative L2: 4.413e-04, L Linfty: 1.430e-03\n",
      "Epoch: 16600, It: 16600, Loss_helmholtz: 2.850e+01\n",
      "Epoch: 16600, It: 16600, Lambda BCs: 0.000e+00, Lambda PDE: 1.868e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16650, It: 16650, Time: 0.80s, Learning Rate: 1.3e-05\n",
      "Epoch: 16650, It: 16650, Total Error: 2.842e+01, Total Loss: 2.364e+00\n",
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      "Epoch: 16650, It: 16650, Loss_helmholtz: 2.842e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16700, It: 16700, Time: 0.80s, Learning Rate: 1.3e-05\n",
      "Epoch: 16700, It: 16700, Total Error: 2.833e+01, Total Loss: 2.357e+00\n",
      "Epoch: 16700, It: 16700, Relative L2: 4.290e-04, L Linfty: 1.399e-03\n",
      "Epoch: 16700, It: 16700, Loss_helmholtz: 2.833e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16750, It: 16750, Time: 0.80s, Learning Rate: 1.3e-05\n",
      "Epoch: 16750, It: 16750, Total Error: 2.825e+01, Total Loss: 2.350e+00\n",
      "Epoch: 16750, It: 16750, Relative L2: 4.247e-04, L Linfty: 1.313e-03\n",
      "Epoch: 16750, It: 16750, Loss_helmholtz: 2.825e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 16800, It: 16800, Time: 0.80s, Learning Rate: 1.2e-05\n",
      "Epoch: 16800, It: 16800, Total Error: 2.817e+01, Total Loss: 2.343e+00\n",
      "Epoch: 16800, It: 16800, Relative L2: 4.343e-04, L Linfty: 1.507e-03\n",
      "Epoch: 16800, It: 16800, Loss_helmholtz: 2.817e+01\n",
      "Epoch: 16800, It: 16800, Lambda BCs: 0.000e+00, Lambda PDE: 1.881e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16850, It: 16850, Time: 0.80s, Learning Rate: 1.2e-05\n",
      "Epoch: 16850, It: 16850, Total Error: 2.808e+01, Total Loss: 2.336e+00\n",
      "Epoch: 16850, It: 16850, Relative L2: 4.289e-04, L Linfty: 1.363e-03\n",
      "Epoch: 16850, It: 16850, Loss_helmholtz: 2.808e+01\n",
      "Epoch: 16850, It: 16850, Lambda BCs: 0.000e+00, Lambda PDE: 1.882e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16900, It: 16900, Time: 0.80s, Learning Rate: 1.2e-05\n",
      "Epoch: 16900, It: 16900, Total Error: 2.800e+01, Total Loss: 2.329e+00\n",
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      "Epoch: 16900, It: 16900, Loss_helmholtz: 2.800e+01\n",
      "Epoch: 16900, It: 16900, Lambda BCs: 0.000e+00, Lambda PDE: 1.882e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 16950, It: 16950, Time: 0.80s, Learning Rate: 1.2e-05\n",
      "Epoch: 16950, It: 16950, Total Error: 2.792e+01, Total Loss: 2.322e+00\n",
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      "Epoch: 16950, It: 16950, Loss_helmholtz: 2.792e+01\n",
      "Epoch: 16950, It: 16950, Lambda BCs: 0.000e+00, Lambda PDE: 1.884e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17000, It: 17000, Time: 0.80s, Learning Rate: 1.2e-05\n",
      "Epoch: 17000, It: 17000, Total Error: 2.784e+01, Total Loss: 2.315e+00\n",
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      "Epoch: 17000, It: 17000, Loss_helmholtz: 2.784e+01\n",
      "Epoch: 17000, It: 17000, Lambda BCs: 0.000e+00, Lambda PDE: 1.887e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17050, It: 17050, Time: 0.81s, Learning Rate: 1.1e-05\n",
      "Epoch: 17050, It: 17050, Total Error: 2.775e+01, Total Loss: 2.309e+00\n",
      "Epoch: 17050, It: 17050, Relative L2: 4.481e-04, L Linfty: 1.400e-03\n",
      "Epoch: 17050, It: 17050, Loss_helmholtz: 2.775e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17100, It: 17100, Time: 0.81s, Learning Rate: 1.1e-05\n",
      "Epoch: 17100, It: 17100, Total Error: 2.767e+01, Total Loss: 2.302e+00\n",
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      "Epoch: 17100, It: 17100, Loss_helmholtz: 2.767e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17150, It: 17150, Time: 0.80s, Learning Rate: 1.1e-05\n",
      "Epoch: 17150, It: 17150, Total Error: 2.759e+01, Total Loss: 2.295e+00\n",
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      "Epoch: 17150, It: 17150, Loss_helmholtz: 2.759e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17200, It: 17200, Time: 0.80s, Learning Rate: 1.1e-05\n",
      "Epoch: 17200, It: 17200, Total Error: 2.751e+01, Total Loss: 2.288e+00\n",
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      "Epoch: 17200, It: 17200, Loss_helmholtz: 2.751e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17250, It: 17250, Time: 0.80s, Learning Rate: 1.1e-05\n",
      "Epoch: 17250, It: 17250, Total Error: 2.743e+01, Total Loss: 2.282e+00\n",
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      "Epoch: 17250, It: 17250, Loss_helmholtz: 2.743e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17300, It: 17300, Time: 0.80s, Learning Rate: 1.0e-05\n",
      "Epoch: 17300, It: 17300, Total Error: 2.735e+01, Total Loss: 2.275e+00\n",
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      "Epoch: 17300, It: 17300, Loss_helmholtz: 2.735e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17350, It: 17350, Time: 0.80s, Learning Rate: 1.0e-05\n",
      "Epoch: 17350, It: 17350, Total Error: 2.728e+01, Total Loss: 2.269e+00\n",
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      "Epoch: 17350, It: 17350, Loss_helmholtz: 2.728e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17400, It: 17400, Time: 0.81s, Learning Rate: 1.0e-05\n",
      "Epoch: 17400, It: 17400, Total Error: 2.720e+01, Total Loss: 2.262e+00\n",
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      "Epoch: 17400, It: 17400, Loss_helmholtz: 2.720e+01\n",
      "Epoch: 17400, It: 17400, Lambda BCs: 0.000e+00, Lambda PDE: 1.892e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17450, It: 17450, Time: 0.80s, Learning Rate: 9.9e-06\n",
      "Epoch: 17450, It: 17450, Total Error: 2.712e+01, Total Loss: 2.256e+00\n",
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      "Epoch: 17450, It: 17450, Loss_helmholtz: 2.712e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17500, It: 17500, Time: 0.80s, Learning Rate: 9.7e-06\n",
      "Epoch: 17500, It: 17500, Total Error: 2.704e+01, Total Loss: 2.249e+00\n",
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      "Epoch: 17500, It: 17500, Loss_helmholtz: 2.704e+01\n",
      "Epoch: 17500, It: 17500, Lambda BCs: 0.000e+00, Lambda PDE: 1.898e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17550, It: 17550, Time: 0.80s, Learning Rate: 9.6e-06\n",
      "Epoch: 17550, It: 17550, Total Error: 2.697e+01, Total Loss: 2.243e+00\n",
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      "Epoch: 17550, It: 17550, Loss_helmholtz: 2.697e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17600, It: 17600, Time: 0.80s, Learning Rate: 9.4e-06\n",
      "Epoch: 17600, It: 17600, Total Error: 2.689e+01, Total Loss: 2.237e+00\n",
      "Epoch: 17600, It: 17600, Relative L2: 4.202e-04, L Linfty: 1.297e-03\n",
      "Epoch: 17600, It: 17600, Loss_helmholtz: 2.689e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17650, It: 17650, Time: 0.80s, Learning Rate: 9.2e-06\n",
      "Epoch: 17650, It: 17650, Total Error: 2.681e+01, Total Loss: 2.230e+00\n",
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      "Epoch: 17650, It: 17650, Loss_helmholtz: 2.681e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17700, It: 17700, Time: 0.80s, Learning Rate: 9.1e-06\n",
      "Epoch: 17700, It: 17700, Total Error: 2.674e+01, Total Loss: 2.224e+00\n",
      "Epoch: 17700, It: 17700, Relative L2: 4.243e-04, L Linfty: 1.255e-03\n",
      "Epoch: 17700, It: 17700, Loss_helmholtz: 2.674e+01\n",
      "Epoch: 17700, It: 17700, Lambda BCs: 0.000e+00, Lambda PDE: 1.909e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17750, It: 17750, Time: 0.80s, Learning Rate: 8.9e-06\n",
      "Epoch: 17750, It: 17750, Total Error: 2.666e+01, Total Loss: 2.218e+00\n",
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      "Epoch: 17750, It: 17750, Loss_helmholtz: 2.666e+01\n",
      "Epoch: 17750, It: 17750, Lambda BCs: 0.000e+00, Lambda PDE: 1.912e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17800, It: 17800, Time: 0.80s, Learning Rate: 8.7e-06\n",
      "Epoch: 17800, It: 17800, Total Error: 2.659e+01, Total Loss: 2.212e+00\n",
      "Epoch: 17800, It: 17800, Relative L2: 4.145e-04, L Linfty: 1.248e-03\n",
      "Epoch: 17800, It: 17800, Loss_helmholtz: 2.659e+01\n",
      "Epoch: 17800, It: 17800, Lambda BCs: 0.000e+00, Lambda PDE: 1.912e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17850, It: 17850, Time: 0.80s, Learning Rate: 8.6e-06\n",
      "Epoch: 17850, It: 17850, Total Error: 2.651e+01, Total Loss: 2.205e+00\n",
      "Epoch: 17850, It: 17850, Relative L2: 4.308e-04, L Linfty: 1.653e-03\n",
      "Epoch: 17850, It: 17850, Loss_helmholtz: 2.651e+01\n",
      "Epoch: 17850, It: 17850, Lambda BCs: 0.000e+00, Lambda PDE: 1.913e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 17900, It: 17900, Time: 0.80s, Learning Rate: 8.4e-06\n",
      "Epoch: 17900, It: 17900, Total Error: 2.644e+01, Total Loss: 2.199e+00\n",
      "Epoch: 17900, It: 17900, Relative L2: 4.205e-04, L Linfty: 1.391e-03\n",
      "Epoch: 17900, It: 17900, Loss_helmholtz: 2.644e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 17950, It: 17950, Time: 0.80s, Learning Rate: 8.3e-06\n",
      "Epoch: 17950, It: 17950, Total Error: 2.637e+01, Total Loss: 2.193e+00\n",
      "Epoch: 17950, It: 17950, Relative L2: 4.265e-04, L Linfty: 1.311e-03\n",
      "Epoch: 17950, It: 17950, Loss_helmholtz: 2.637e+01\n",
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      "--------------------------------------------------\n",
      "Epoch: 18000, It: 18000, Time: 0.80s, Learning Rate: 8.1e-06\n",
      "Epoch: 18000, It: 18000, Total Error: 2.629e+01, Total Loss: 2.187e+00\n",
      "Epoch: 18000, It: 18000, Relative L2: 4.332e-04, L Linfty: 1.397e-03\n",
      "Epoch: 18000, It: 18000, Loss_helmholtz: 2.629e+01\n",
      "Epoch: 18000, It: 18000, Lambda BCs: 0.000e+00, Lambda PDE: 1.919e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18050, It: 18050, Time: 0.80s, Learning Rate: 8.0e-06\n",
      "Epoch: 18050, It: 18050, Total Error: 2.622e+01, Total Loss: 2.181e+00\n",
      "Epoch: 18050, It: 18050, Relative L2: 4.409e-04, L Linfty: 1.509e-03\n",
      "Epoch: 18050, It: 18050, Loss_helmholtz: 2.622e+01\n",
      "Epoch: 18050, It: 18050, Lambda BCs: 0.000e+00, Lambda PDE: 1.919e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18100, It: 18100, Time: 0.80s, Learning Rate: 7.9e-06\n",
      "Epoch: 18100, It: 18100, Total Error: 2.615e+01, Total Loss: 2.175e+00\n",
      "Epoch: 18100, It: 18100, Relative L2: 4.267e-04, L Linfty: 1.455e-03\n",
      "Epoch: 18100, It: 18100, Loss_helmholtz: 2.615e+01\n",
      "Epoch: 18100, It: 18100, Lambda BCs: 0.000e+00, Lambda PDE: 1.920e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18150, It: 18150, Time: 0.80s, Learning Rate: 7.7e-06\n",
      "Epoch: 18150, It: 18150, Total Error: 2.608e+01, Total Loss: 2.169e+00\n",
      "Epoch: 18150, It: 18150, Relative L2: 4.194e-04, L Linfty: 1.378e-03\n",
      "Epoch: 18150, It: 18150, Loss_helmholtz: 2.608e+01\n",
      "Epoch: 18150, It: 18150, Lambda BCs: 0.000e+00, Lambda PDE: 1.921e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18200, It: 18200, Time: 0.80s, Learning Rate: 7.6e-06\n",
      "Epoch: 18200, It: 18200, Total Error: 2.601e+01, Total Loss: 2.163e+00\n",
      "Epoch: 18200, It: 18200, Relative L2: 4.274e-04, L Linfty: 1.517e-03\n",
      "Epoch: 18200, It: 18200, Loss_helmholtz: 2.601e+01\n",
      "Epoch: 18200, It: 18200, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18250, It: 18250, Time: 0.80s, Learning Rate: 7.4e-06\n",
      "Epoch: 18250, It: 18250, Total Error: 2.593e+01, Total Loss: 2.157e+00\n",
      "Epoch: 18250, It: 18250, Relative L2: 4.318e-04, L Linfty: 1.626e-03\n",
      "Epoch: 18250, It: 18250, Loss_helmholtz: 2.593e+01\n",
      "Epoch: 18250, It: 18250, Lambda BCs: 0.000e+00, Lambda PDE: 1.926e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18300, It: 18300, Time: 0.80s, Learning Rate: 7.3e-06\n",
      "Epoch: 18300, It: 18300, Total Error: 2.586e+01, Total Loss: 2.151e+00\n",
      "Epoch: 18300, It: 18300, Relative L2: 4.164e-04, L Linfty: 1.540e-03\n",
      "Epoch: 18300, It: 18300, Loss_helmholtz: 2.586e+01\n",
      "Epoch: 18300, It: 18300, Lambda BCs: 0.000e+00, Lambda PDE: 1.927e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18350, It: 18350, Time: 0.80s, Learning Rate: 7.2e-06\n",
      "Epoch: 18350, It: 18350, Total Error: 2.579e+01, Total Loss: 2.146e+00\n",
      "Epoch: 18350, It: 18350, Relative L2: 4.292e-04, L Linfty: 1.363e-03\n",
      "Epoch: 18350, It: 18350, Loss_helmholtz: 2.579e+01\n",
      "Epoch: 18350, It: 18350, Lambda BCs: 0.000e+00, Lambda PDE: 1.928e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18400, It: 18400, Time: 0.80s, Learning Rate: 7.1e-06\n",
      "Epoch: 18400, It: 18400, Total Error: 2.572e+01, Total Loss: 2.140e+00\n",
      "Epoch: 18400, It: 18400, Relative L2: 4.200e-04, L Linfty: 1.435e-03\n",
      "Epoch: 18400, It: 18400, Loss_helmholtz: 2.572e+01\n",
      "Epoch: 18400, It: 18400, Lambda BCs: 0.000e+00, Lambda PDE: 1.927e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18450, It: 18450, Time: 0.80s, Learning Rate: 6.9e-06\n",
      "Epoch: 18450, It: 18450, Total Error: 2.565e+01, Total Loss: 2.134e+00\n",
      "Epoch: 18450, It: 18450, Relative L2: 4.442e-04, L Linfty: 1.505e-03\n",
      "Epoch: 18450, It: 18450, Loss_helmholtz: 2.565e+01\n",
      "Epoch: 18450, It: 18450, Lambda BCs: 0.000e+00, Lambda PDE: 1.925e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18500, It: 18500, Time: 0.80s, Learning Rate: 6.8e-06\n",
      "Epoch: 18500, It: 18500, Total Error: 2.559e+01, Total Loss: 2.128e+00\n",
      "Epoch: 18500, It: 18500, Relative L2: 4.399e-04, L Linfty: 1.327e-03\n",
      "Epoch: 18500, It: 18500, Loss_helmholtz: 2.559e+01\n",
      "Epoch: 18500, It: 18500, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18550, It: 18550, Time: 0.80s, Learning Rate: 6.7e-06\n",
      "Epoch: 18550, It: 18550, Total Error: 2.552e+01, Total Loss: 2.123e+00\n",
      "Epoch: 18550, It: 18550, Relative L2: 4.346e-04, L Linfty: 1.310e-03\n",
      "Epoch: 18550, It: 18550, Loss_helmholtz: 2.552e+01\n",
      "Epoch: 18550, It: 18550, Lambda BCs: 0.000e+00, Lambda PDE: 1.922e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18600, It: 18600, Time: 0.80s, Learning Rate: 6.6e-06\n",
      "Epoch: 18600, It: 18600, Total Error: 2.545e+01, Total Loss: 2.117e+00\n",
      "Epoch: 18600, It: 18600, Relative L2: 4.145e-04, L Linfty: 1.314e-03\n",
      "Epoch: 18600, It: 18600, Loss_helmholtz: 2.545e+01\n",
      "Epoch: 18600, It: 18600, Lambda BCs: 0.000e+00, Lambda PDE: 1.920e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18650, It: 18650, Time: 0.80s, Learning Rate: 6.5e-06\n",
      "Epoch: 18650, It: 18650, Total Error: 2.538e+01, Total Loss: 2.111e+00\n",
      "Epoch: 18650, It: 18650, Relative L2: 4.128e-04, L Linfty: 1.312e-03\n",
      "Epoch: 18650, It: 18650, Loss_helmholtz: 2.538e+01\n",
      "Epoch: 18650, It: 18650, Lambda BCs: 0.000e+00, Lambda PDE: 1.921e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18700, It: 18700, Time: 0.80s, Learning Rate: 6.3e-06\n",
      "Epoch: 18700, It: 18700, Total Error: 2.531e+01, Total Loss: 2.106e+00\n",
      "Epoch: 18700, It: 18700, Relative L2: 4.134e-04, L Linfty: 1.310e-03\n",
      "Epoch: 18700, It: 18700, Loss_helmholtz: 2.531e+01\n",
      "Epoch: 18700, It: 18700, Lambda BCs: 0.000e+00, Lambda PDE: 1.926e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18750, It: 18750, Time: 0.80s, Learning Rate: 6.2e-06\n",
      "Epoch: 18750, It: 18750, Total Error: 2.525e+01, Total Loss: 2.100e+00\n",
      "Epoch: 18750, It: 18750, Relative L2: 4.126e-04, L Linfty: 1.405e-03\n",
      "Epoch: 18750, It: 18750, Loss_helmholtz: 2.525e+01\n",
      "Epoch: 18750, It: 18750, Lambda BCs: 0.000e+00, Lambda PDE: 1.930e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18800, It: 18800, Time: 0.83s, Learning Rate: 6.1e-06\n",
      "Epoch: 18800, It: 18800, Total Error: 2.518e+01, Total Loss: 2.094e+00\n",
      "Epoch: 18800, It: 18800, Relative L2: 4.128e-04, L Linfty: 1.387e-03\n",
      "Epoch: 18800, It: 18800, Loss_helmholtz: 2.518e+01\n",
      "Epoch: 18800, It: 18800, Lambda BCs: 0.000e+00, Lambda PDE: 1.933e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18850, It: 18850, Time: 0.84s, Learning Rate: 6.0e-06\n",
      "Epoch: 18850, It: 18850, Total Error: 2.511e+01, Total Loss: 2.089e+00\n",
      "Epoch: 18850, It: 18850, Relative L2: 4.145e-04, L Linfty: 1.291e-03\n",
      "Epoch: 18850, It: 18850, Loss_helmholtz: 2.511e+01\n",
      "Epoch: 18850, It: 18850, Lambda BCs: 0.000e+00, Lambda PDE: 1.937e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18900, It: 18900, Time: 0.81s, Learning Rate: 5.9e-06\n",
      "Epoch: 18900, It: 18900, Total Error: 2.505e+01, Total Loss: 2.083e+00\n",
      "Epoch: 18900, It: 18900, Relative L2: 4.145e-04, L Linfty: 1.334e-03\n",
      "Epoch: 18900, It: 18900, Loss_helmholtz: 2.505e+01\n",
      "Epoch: 18900, It: 18900, Lambda BCs: 0.000e+00, Lambda PDE: 1.938e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 18950, It: 18950, Time: 0.80s, Learning Rate: 5.8e-06\n",
      "Epoch: 18950, It: 18950, Total Error: 2.498e+01, Total Loss: 2.078e+00\n",
      "Epoch: 18950, It: 18950, Relative L2: 4.148e-04, L Linfty: 1.466e-03\n",
      "Epoch: 18950, It: 18950, Loss_helmholtz: 2.498e+01\n",
      "Epoch: 18950, It: 18950, Lambda BCs: 0.000e+00, Lambda PDE: 1.938e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19000, It: 19000, Time: 0.80s, Learning Rate: 5.7e-06\n",
      "Epoch: 19000, It: 19000, Total Error: 2.491e+01, Total Loss: 2.072e+00\n",
      "Epoch: 19000, It: 19000, Relative L2: 4.136e-04, L Linfty: 1.466e-03\n",
      "Epoch: 19000, It: 19000, Loss_helmholtz: 2.491e+01\n",
      "Epoch: 19000, It: 19000, Lambda BCs: 0.000e+00, Lambda PDE: 1.936e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19050, It: 19050, Time: 0.80s, Learning Rate: 5.6e-06\n",
      "Epoch: 19050, It: 19050, Total Error: 2.485e+01, Total Loss: 2.067e+00\n",
      "Epoch: 19050, It: 19050, Relative L2: 4.117e-04, L Linfty: 1.402e-03\n",
      "Epoch: 19050, It: 19050, Loss_helmholtz: 2.485e+01\n",
      "Epoch: 19050, It: 19050, Lambda BCs: 0.000e+00, Lambda PDE: 1.931e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19100, It: 19100, Time: 0.80s, Learning Rate: 5.5e-06\n",
      "Epoch: 19100, It: 19100, Total Error: 2.478e+01, Total Loss: 2.062e+00\n",
      "Epoch: 19100, It: 19100, Relative L2: 4.165e-04, L Linfty: 1.546e-03\n",
      "Epoch: 19100, It: 19100, Loss_helmholtz: 2.478e+01\n",
      "Epoch: 19100, It: 19100, Lambda BCs: 0.000e+00, Lambda PDE: 1.927e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19150, It: 19150, Time: 0.80s, Learning Rate: 5.4e-06\n",
      "Epoch: 19150, It: 19150, Total Error: 2.472e+01, Total Loss: 2.056e+00\n",
      "Epoch: 19150, It: 19150, Relative L2: 4.134e-04, L Linfty: 1.420e-03\n",
      "Epoch: 19150, It: 19150, Loss_helmholtz: 2.472e+01\n",
      "Epoch: 19150, It: 19150, Lambda BCs: 0.000e+00, Lambda PDE: 1.928e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19200, It: 19200, Time: 0.80s, Learning Rate: 5.3e-06\n",
      "Epoch: 19200, It: 19200, Total Error: 2.466e+01, Total Loss: 2.051e+00\n",
      "Epoch: 19200, It: 19200, Relative L2: 4.195e-04, L Linfty: 1.443e-03\n",
      "Epoch: 19200, It: 19200, Loss_helmholtz: 2.466e+01\n",
      "Epoch: 19200, It: 19200, Lambda BCs: 0.000e+00, Lambda PDE: 1.928e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19250, It: 19250, Time: 0.80s, Learning Rate: 5.2e-06\n",
      "Epoch: 19250, It: 19250, Total Error: 2.459e+01, Total Loss: 2.046e+00\n",
      "Epoch: 19250, It: 19250, Relative L2: 4.114e-04, L Linfty: 1.299e-03\n",
      "Epoch: 19250, It: 19250, Loss_helmholtz: 2.459e+01\n",
      "Epoch: 19250, It: 19250, Lambda BCs: 0.000e+00, Lambda PDE: 1.927e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19300, It: 19300, Time: 0.80s, Learning Rate: 5.1e-06\n",
      "Epoch: 19300, It: 19300, Total Error: 2.453e+01, Total Loss: 2.040e+00\n",
      "Epoch: 19300, It: 19300, Relative L2: 4.124e-04, L Linfty: 1.358e-03\n",
      "Epoch: 19300, It: 19300, Loss_helmholtz: 2.453e+01\n",
      "Epoch: 19300, It: 19300, Lambda BCs: 0.000e+00, Lambda PDE: 1.928e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19350, It: 19350, Time: 0.80s, Learning Rate: 5.0e-06\n",
      "Epoch: 19350, It: 19350, Total Error: 2.446e+01, Total Loss: 2.035e+00\n",
      "Epoch: 19350, It: 19350, Relative L2: 4.142e-04, L Linfty: 1.330e-03\n",
      "Epoch: 19350, It: 19350, Loss_helmholtz: 2.446e+01\n",
      "Epoch: 19350, It: 19350, Lambda BCs: 0.000e+00, Lambda PDE: 1.928e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19400, It: 19400, Time: 0.80s, Learning Rate: 4.9e-06\n",
      "Epoch: 19400, It: 19400, Total Error: 2.440e+01, Total Loss: 2.030e+00\n",
      "Epoch: 19400, It: 19400, Relative L2: 4.126e-04, L Linfty: 1.284e-03\n",
      "Epoch: 19400, It: 19400, Loss_helmholtz: 2.440e+01\n",
      "Epoch: 19400, It: 19400, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19450, It: 19450, Time: 0.80s, Learning Rate: 4.9e-06\n",
      "Epoch: 19450, It: 19450, Total Error: 2.434e+01, Total Loss: 2.025e+00\n",
      "Epoch: 19450, It: 19450, Relative L2: 4.114e-04, L Linfty: 1.518e-03\n",
      "Epoch: 19450, It: 19450, Loss_helmholtz: 2.434e+01\n",
      "Epoch: 19450, It: 19450, Lambda BCs: 0.000e+00, Lambda PDE: 1.919e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19500, It: 19500, Time: 0.80s, Learning Rate: 4.8e-06\n",
      "Epoch: 19500, It: 19500, Total Error: 2.428e+01, Total Loss: 2.019e+00\n",
      "Epoch: 19500, It: 19500, Relative L2: 4.121e-04, L Linfty: 1.283e-03\n",
      "Epoch: 19500, It: 19500, Loss_helmholtz: 2.428e+01\n",
      "Epoch: 19500, It: 19500, Lambda BCs: 0.000e+00, Lambda PDE: 1.913e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19550, It: 19550, Time: 0.80s, Learning Rate: 4.7e-06\n",
      "Epoch: 19550, It: 19550, Total Error: 2.421e+01, Total Loss: 2.014e+00\n",
      "Epoch: 19550, It: 19550, Relative L2: 4.129e-04, L Linfty: 1.359e-03\n",
      "Epoch: 19550, It: 19550, Loss_helmholtz: 2.421e+01\n",
      "Epoch: 19550, It: 19550, Lambda BCs: 0.000e+00, Lambda PDE: 1.913e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19600, It: 19600, Time: 0.80s, Learning Rate: 4.6e-06\n",
      "Epoch: 19600, It: 19600, Total Error: 2.415e+01, Total Loss: 2.009e+00\n",
      "Epoch: 19600, It: 19600, Relative L2: 4.126e-04, L Linfty: 1.399e-03\n",
      "Epoch: 19600, It: 19600, Loss_helmholtz: 2.415e+01\n",
      "Epoch: 19600, It: 19600, Lambda BCs: 0.000e+00, Lambda PDE: 1.915e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19650, It: 19650, Time: 0.80s, Learning Rate: 4.5e-06\n",
      "Epoch: 19650, It: 19650, Total Error: 2.409e+01, Total Loss: 2.004e+00\n",
      "Epoch: 19650, It: 19650, Relative L2: 4.139e-04, L Linfty: 1.359e-03\n",
      "Epoch: 19650, It: 19650, Loss_helmholtz: 2.409e+01\n",
      "Epoch: 19650, It: 19650, Lambda BCs: 0.000e+00, Lambda PDE: 1.917e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19700, It: 19700, Time: 0.80s, Learning Rate: 4.4e-06\n",
      "Epoch: 19700, It: 19700, Total Error: 2.403e+01, Total Loss: 1.999e+00\n",
      "Epoch: 19700, It: 19700, Relative L2: 4.126e-04, L Linfty: 1.381e-03\n",
      "Epoch: 19700, It: 19700, Loss_helmholtz: 2.403e+01\n",
      "Epoch: 19700, It: 19700, Lambda BCs: 0.000e+00, Lambda PDE: 1.915e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19750, It: 19750, Time: 0.80s, Learning Rate: 4.4e-06\n",
      "Epoch: 19750, It: 19750, Total Error: 2.397e+01, Total Loss: 1.994e+00\n",
      "Epoch: 19750, It: 19750, Relative L2: 4.124e-04, L Linfty: 1.504e-03\n",
      "Epoch: 19750, It: 19750, Loss_helmholtz: 2.397e+01\n",
      "Epoch: 19750, It: 19750, Lambda BCs: 0.000e+00, Lambda PDE: 1.916e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19800, It: 19800, Time: 0.80s, Learning Rate: 4.3e-06\n",
      "Epoch: 19800, It: 19800, Total Error: 2.391e+01, Total Loss: 1.989e+00\n",
      "Epoch: 19800, It: 19800, Relative L2: 4.133e-04, L Linfty: 1.315e-03\n",
      "Epoch: 19800, It: 19800, Loss_helmholtz: 2.391e+01\n",
      "Epoch: 19800, It: 19800, Lambda BCs: 0.000e+00, Lambda PDE: 1.914e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19850, It: 19850, Time: 0.80s, Learning Rate: 4.2e-06\n",
      "Epoch: 19850, It: 19850, Total Error: 2.385e+01, Total Loss: 1.984e+00\n",
      "Epoch: 19850, It: 19850, Relative L2: 4.223e-04, L Linfty: 1.361e-03\n",
      "Epoch: 19850, It: 19850, Loss_helmholtz: 2.385e+01\n",
      "Epoch: 19850, It: 19850, Lambda BCs: 0.000e+00, Lambda PDE: 1.914e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19900, It: 19900, Time: 0.80s, Learning Rate: 4.1e-06\n",
      "Epoch: 19900, It: 19900, Total Error: 2.379e+01, Total Loss: 1.979e+00\n",
      "Epoch: 19900, It: 19900, Relative L2: 4.231e-04, L Linfty: 1.322e-03\n",
      "Epoch: 19900, It: 19900, Loss_helmholtz: 2.379e+01\n",
      "Epoch: 19900, It: 19900, Lambda BCs: 0.000e+00, Lambda PDE: 1.914e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 19950, It: 19950, Time: 0.80s, Learning Rate: 4.1e-06\n",
      "Epoch: 19950, It: 19950, Total Error: 2.373e+01, Total Loss: 1.974e+00\n",
      "Epoch: 19950, It: 19950, Relative L2: 4.145e-04, L Linfty: 1.341e-03\n",
      "Epoch: 19950, It: 19950, Loss_helmholtz: 2.373e+01\n",
      "Epoch: 19950, It: 19950, Lambda BCs: 0.000e+00, Lambda PDE: 1.916e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20000, It: 20000, Time: 0.80s, Learning Rate: 4.0e-06\n",
      "Epoch: 20000, It: 20000, Total Error: 2.367e+01, Total Loss: 1.969e+00\n",
      "Epoch: 20000, It: 20000, Relative L2: 4.146e-04, L Linfty: 1.399e-03\n",
      "Epoch: 20000, It: 20000, Loss_helmholtz: 2.367e+01\n",
      "Epoch: 20000, It: 20000, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n"
     ]
    }
   ],
   "source": [
    "log_loss     = []\n",
    "log_epoch    = []\n",
    "log_lambdas  = []\n",
    "epoch_losses = []\n",
    "log_u_pred   = []\n",
    "log_res      = []\n",
    "starting_epoch=0\n",
    "M1= Norm_metric1(X_res[:, :2])\n",
    "M2= Norm_metric2(X_res[:, :2])\n",
    "Errors=np.zeros((1,3))\n",
    "errors=np.zeros((1,3))\n",
    "start_time = time.time()\n",
    "start_time2 = time.time()\n",
    "for epoch in range(1, num_epochs_adam+1):\n",
    "    #idx_res=np.random.choice(len(X_res),int(len(X_res)*1)) \n",
    "    idx_res=np.arange(len(X_res))\n",
    "    batch_data =   (X_BCs_U,\n",
    "                    X_BCs_L,\n",
    "                    X_BCs_R,\n",
    "                    X_BCs_Lf,\n",
    "                    X_res)\n",
    "    params, lambdas, opt_state_w, batch_losses, grad_w = update(\n",
    "    params, lambdas, opt_state_w, batch_data,idx_res,epoch)\n",
    "\n",
    "    if epoch==1 or (epoch%50)==0:\n",
    "        end_time = time.time()\n",
    "        #Get Losses\n",
    "        epoch_losses.append(batch_losses)\n",
    "        loss_avg: np.ndarray = np.mean(np.array(jax.device_get(epoch_losses)), axis=0)\n",
    "        (\n",
    "                loss,\n",
    "                loss_helmholtz,\n",
    "        ) = loss_avg\n",
    "        log_lambdas.append(jax.device_get(lambdas))     \n",
    "        # Learnig Rate\n",
    "        optim_step =  epoch\n",
    "        lr = lr0 * decay_rate ** (optim_step / decay_step)\n",
    "        ##Store\n",
    "        log_epoch.append(epoch)\n",
    "        log_loss.append(loss_avg)\n",
    "        #Get Errors\n",
    "        u_pred,e_helmholtz,errors,Errors=get_errors(epoch,Errors,num_epochs_adam,params,\n",
    "                                                    X_exact)\n",
    "        log_u_pred.append(u_pred)\n",
    "        log_res.append(e_helmholtz)\n",
    "        Error    =(\n",
    "                +loss_helmholtz)\n",
    "        lambda_bcs_print=np.mean(lamB*lambdas['BCs'])\n",
    "        lambda_res_print=np.mean(lamE*lambdas['Res'])\n",
    "\n",
    "        print(\"-\" * 50)\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Time: {end_time-start_time:.2f}s, Learning Rate: {lr:.1e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Total Error: {Error:.3e}, Total Loss: {loss:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Relative L2: {errors[0,0]:.3e}, L Linfty: {errors[0,1]:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Loss_helmholtz: {loss_helmholtz:.3e}\")\n",
    "        print(\n",
    "        f\"Epoch: {epoch:d}, It: {optim_step:d}, Lambda BCs: {lambda_bcs_print:.3e}, Lambda PDE: {lambda_res_print:.3e}\")\n",
    "\n",
    "        start_time = time.time()\n",
    "\n",
    "end_time2 = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e089f02",
   "metadata": {},
   "source": [
    "# LBFGs Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "27be7536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For L-BFGS-B\n",
    "def get_losses(params,X_res):\n",
    "    lamE_it=use_RBA*lambdas['Res']+(1-use_RBA)\n",
    "    lamB_it=1\n",
    "    # Call Functions\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_up          = X_BCs_U[:, :2]\n",
    "    u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "    # BCs prediction \n",
    "    tx_low        = X_BCs_L[:, :2]\n",
    "    u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_r        = X_BCs_R[:, :2]\n",
    "    u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_lft        = X_BCs_Lf[:, :2]\n",
    "    u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "    \n",
    "    #Loss Equation\n",
    "    loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "    #Loss Bcs u\n",
    "    loss_up                  = lamB*Error_fn(u_pred_bcs_up,0.0,weight=lamB_it)\n",
    "    loss_low                 = lamB*Error_fn(u_pred_bcs_low,0.0,weight=lamB_it)\n",
    "    loss_r                   = lamB*Error_fn(u_pred_bcs_r,0.0,weight=lamB_it)\n",
    "    loss_lf                  = lamB*Error_fn(u_pred_bcs_lft,0.0,weight=lamB_it)\n",
    "    #Total Loss\n",
    "    loss                = (        \n",
    "                            + loss_helmholtz\n",
    "                            + loss_up\n",
    "                            + loss_low\n",
    "                            + loss_r\n",
    "                            + loss_lf\n",
    "                            )\n",
    "    all_losses=[\n",
    "        loss,\n",
    "        Error_fn(e_helmholtz,0.0),\n",
    "        jnp.mean(jnp.square(u_pred_bcs_up)\n",
    "                    +jnp.square(u_pred_bcs_low)\n",
    "                    +jnp.square(u_pred_bcs_up)\n",
    "                    +jnp.square(u_pred_bcs_up)),]\n",
    "    return all_losses\n",
    "\n",
    "def loss_fn(params):\n",
    "    lamE_it=use_RBA*lambdas['Res']+(1-use_RBA)\n",
    "    lamB_it=1\n",
    "    # Call Functions\n",
    "    eq1                 = PDE(params)\n",
    "    u_fx                = u_model(params)\n",
    "    # Residuals prediction\n",
    "    e_helmholtz         = vmap(eq1, (0))(X_res)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_up          = X_BCs_U[:, :2]\n",
    "    u_pred_bcs_up  = vmap(u_fx, (0))(tx_up)[:,None]\n",
    "    # BCs prediction \n",
    "    tx_low        = X_BCs_L[:, :2]\n",
    "    u_pred_bcs_low= vmap(u_fx, (0))(tx_low)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_r        = X_BCs_R[:, :2]\n",
    "    u_pred_bcs_r= vmap(u_fx, (0))(tx_r)[:,None]\n",
    "\n",
    "    # BCs prediction \n",
    "    tx_lft        = X_BCs_Lf[:, :2]\n",
    "    u_pred_bcs_lft= vmap(u_fx, (0))(tx_lft)[:,None]\n",
    "    \n",
    "    #Loss Equation\n",
    "    loss_helmholtz           = lamE*Error_fn(e_helmholtz,0.0,weight=lamE_it)\n",
    "    #Loss Bcs u\n",
    "    loss_up                  = lamB*Error_fn(u_pred_bcs_up,0.0,weight=lamB_it)\n",
    "    loss_low                 = lamB*Error_fn(u_pred_bcs_low,0.0,weight=lamB_it)\n",
    "    loss_r                   = lamB*Error_fn(u_pred_bcs_r,0.0,weight=lamB_it)\n",
    "    loss_lf                  = lamB*Error_fn(u_pred_bcs_lft,0.0,weight=lamB_it)\n",
    "    #Total Loss\n",
    "    loss                = (        \n",
    "                            + loss_helmholtz\n",
    "                            + loss_up\n",
    "                            + loss_low\n",
    "                            + loss_r\n",
    "                            + loss_lf\n",
    "                            )\n",
    "    return loss\n",
    "            \n",
    "def Callback_Loss(params_i):\n",
    "    global log_loss,log_lambdas\n",
    "    global log_epoch\n",
    "    global epoch\n",
    "    global start_time\n",
    "    global Errors\n",
    "    global Mu\n",
    "    global Sigmas\n",
    "    global lambdas,gamma,lr_lambdas_0\n",
    "    if  epoch%1==0:\n",
    "        if  epoch%100==0:\n",
    "            end_time = time.time()\n",
    "            all_losses=get_losses(params_i,X_res)\n",
    "            loss           =all_losses[0]\n",
    "            loss_helmholtz =all_losses[1]\n",
    "            loss_u_bcs     =all_losses[2]\n",
    "            # Learnig Rate\n",
    "            optim_step =  epoch\n",
    "            ##Store\n",
    "            log_epoch.append(epoch)\n",
    "            #Plot Lambdas\n",
    "            u_pred,e_helmholtz,errors,Errors=get_errors(epoch,Errors,\n",
    "                                                        num_epochs_adam+num_epochs_lbfgsb,\n",
    "                                                        params_i,\n",
    "                                                        X_exact,)\n",
    "            log_lambdas.append(lambdas)\n",
    "            log_u_pred.append(u_pred)\n",
    "            log_res.append(e_helmholtz)\n",
    "            Error    =(\n",
    "                    +loss_helmholtz\n",
    "                    +loss_u_bcs)\n",
    "            lambda_bcs_print=np.mean(lamB*lambdas['BCs'])\n",
    "            lambda_res_print=np.mean(lamE*lambdas['Res'])\n",
    "            print(\"-\" * 50)\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Time: {end_time-start_time:.2f}s, Learning Rate: {lr:.1e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Total Error: {Error:.3e}, Total Loss: {loss:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Relative L2: {errors[0,0]:.3e}, Relative Linfty: {errors[0,1]:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Loss_helmholtz: {loss_helmholtz:.3e}, Loss BCs(u): {loss_u_bcs:.3e}\")\n",
    "            print(\n",
    "            f\"Epoch: {epoch:d}, It: {optim_step:d}, Lambda BCs: {lambda_bcs_print:.3e}, Lambda PDE: {lambda_res_print:.3e}\")\n",
    "\n",
    "            start_time = time.time()\n",
    "    epoch += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87e513f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1:\n",
      "--------------------------------------------------\n",
      "Epoch: 20000, It: 20000, Time: 12.22s, Learning Rate: 4.0e-06\n",
      "Epoch: 20000, It: 20000, Total Error: 3.754e-04, Total Loss: 1.162e-03\n",
      "Epoch: 20000, It: 20000, Relative L2: 1.443e-04, Relative Linfty: 2.312e-04\n",
      "Epoch: 20000, It: 20000, Loss_helmholtz: 3.754e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20000, It: 20000, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Epoch: 20100, It: 20100, Time: 15.68s, Learning Rate: 4.0e-06\n",
      "Epoch: 20100, It: 20100, Total Error: 3.375e-04, Total Loss: 1.081e-03\n",
      "Epoch: 20100, It: 20100, Relative L2: 5.549e-05, Relative Linfty: 9.668e-05\n",
      "Epoch: 20100, It: 20100, Loss_helmholtz: 3.375e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20100, It: 20100, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20200, It: 20200, Time: 15.37s, Learning Rate: 4.0e-06\n",
      "Epoch: 20200, It: 20200, Total Error: 3.356e-04, Total Loss: 1.076e-03\n",
      "Epoch: 20200, It: 20200, Relative L2: 5.684e-05, Relative Linfty: 9.970e-05\n",
      "Epoch: 20200, It: 20200, Loss_helmholtz: 3.356e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20200, It: 20200, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "Step 2:\n",
      "--------------------------------------------------\n",
      "Epoch: 20300, It: 20300, Time: 15.65s, Learning Rate: 4.0e-06\n",
      "Epoch: 20300, It: 20300, Total Error: 3.345e-04, Total Loss: 1.073e-03\n",
      "Epoch: 20300, It: 20300, Relative L2: 5.512e-05, Relative Linfty: 1.098e-04\n",
      "Epoch: 20300, It: 20300, Loss_helmholtz: 3.345e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20300, It: 20300, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20400, It: 20400, Time: 15.37s, Learning Rate: 4.0e-06\n",
      "Epoch: 20400, It: 20400, Total Error: 3.334e-04, Total Loss: 1.066e-03\n",
      "Epoch: 20400, It: 20400, Relative L2: 8.046e-05, Relative Linfty: 1.443e-04\n",
      "Epoch: 20400, It: 20400, Loss_helmholtz: 3.334e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20400, It: 20400, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "Step 3:\n",
      "--------------------------------------------------\n",
      "Epoch: 20500, It: 20500, Time: 15.88s, Learning Rate: 4.0e-06\n",
      "Epoch: 20500, It: 20500, Total Error: 3.307e-04, Total Loss: 1.060e-03\n",
      "Epoch: 20500, It: 20500, Relative L2: 6.376e-05, Relative Linfty: 1.100e-04\n",
      "Epoch: 20500, It: 20500, Loss_helmholtz: 3.307e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20500, It: 20500, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20600, It: 20600, Time: 15.17s, Learning Rate: 4.0e-06\n",
      "Epoch: 20600, It: 20600, Total Error: 3.301e-04, Total Loss: 1.057e-03\n",
      "Epoch: 20600, It: 20600, Relative L2: 5.362e-05, Relative Linfty: 9.676e-05\n",
      "Epoch: 20600, It: 20600, Loss_helmholtz: 3.301e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20600, It: 20600, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "Step 4:\n",
      "--------------------------------------------------\n",
      "Epoch: 20700, It: 20700, Time: 16.02s, Learning Rate: 4.0e-06\n",
      "Epoch: 20700, It: 20700, Total Error: 3.288e-04, Total Loss: 1.054e-03\n",
      "Epoch: 20700, It: 20700, Relative L2: 5.955e-05, Relative Linfty: 1.055e-04\n",
      "Epoch: 20700, It: 20700, Loss_helmholtz: 3.288e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20700, It: 20700, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 20800, It: 20800, Time: 15.89s, Learning Rate: 4.0e-06\n",
      "Epoch: 20800, It: 20800, Total Error: 3.286e-04, Total Loss: 1.049e-03\n",
      "Epoch: 20800, It: 20800, Relative L2: 8.033e-05, Relative Linfty: 1.376e-04\n",
      "Epoch: 20800, It: 20800, Loss_helmholtz: 3.286e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20800, It: 20800, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "Step 5:\n",
      "--------------------------------------------------\n",
      "Epoch: 20900, It: 20900, Time: 15.77s, Learning Rate: 4.0e-06\n",
      "Epoch: 20900, It: 20900, Total Error: 3.259e-04, Total Loss: 1.043e-03\n",
      "Epoch: 20900, It: 20900, Relative L2: 5.489e-05, Relative Linfty: 1.065e-04\n",
      "Epoch: 20900, It: 20900, Loss_helmholtz: 3.259e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 20900, It: 20900, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n",
      "--------------------------------------------------\n",
      "Epoch: 21000, It: 21000, Time: 15.08s, Learning Rate: 4.0e-06\n",
      "Epoch: 21000, It: 21000, Total Error: 3.249e-04, Total Loss: 1.040e-03\n",
      "Epoch: 21000, It: 21000, Relative L2: 6.207e-05, Relative Linfty: 1.045e-04\n",
      "Epoch: 21000, It: 21000, Loss_helmholtz: 3.249e-04, Loss BCs(u): 0.000e+00\n",
      "Epoch: 21000, It: 21000, Lambda BCs: 0.000e+00, Lambda PDE: 1.924e+00\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "optimizer2=jaxopt.ScipyMinimize(method='L-BFGS-B',\n",
    "                               fun=loss_fn,\n",
    "                               callback=Callback_Loss,\n",
    "                               maxiter=int(num_epochs_lbfgsb/substepts_lbfgs)+1,\n",
    "                               jit=True,\n",
    "                               tol= 1e-99,\n",
    "                               dtype=np.float64)\n",
    "if num_epochs_lbfgsb>0:\n",
    "    config.update(\"jax_enable_x64\", True)\n",
    "    for i in range(substepts_lbfgs):\n",
    "        print(f'Step {i+1}:')\n",
    "        params,opt_state=optimizer2.run(params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4cbda87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final RL2 error: 5.855e-05\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_fx                = u_model(params)\n",
    "#prediction \n",
    "xy, u_real          = X_exact[:, :2], X_exact[:, 2:]\n",
    "u_pred              = vmap(u_fx, (0))(xy)[:,None]\n",
    "u_pred              =jax.device_get(u_pred)\n",
    "a=relative_error2(u_pred.flatten(),u_real.flatten())\n",
    "print(f'Final RL2 error: {a:.3e}')\n",
    "U_pred=np.reshape(u_pred,(nx,ny))\n",
    "Error=np.abs(u-U_pred)\n",
    "Exact=np.array([u,U_pred,Error])  \n",
    "plot3D_mat(X,Y,Exact,f_names=['Actual u(x,y)','Predicted u(x,y)',f'Relative L2={a:.3e}'],cmap='RdBu_r',window=5,font_size=14)\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3681a0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 412/412 [00:00<00:00, 2079.54it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_res_min=[]\n",
    "lambda_res_max=[]\n",
    "lambda_res_mean=[]\n",
    "mpl.rcParams['font.size'] = 16\n",
    "for i in tqdm(range(len(log_lambdas))):\n",
    "    lambda_res_min.append(lamE*np.min(log_lambdas[i]['Res']))\n",
    "    lambda_res_max.append(lamE*np.max(log_lambdas[i]['Res']))\n",
    "    lambda_res_mean.append(lamE*np.mean(log_lambdas[i]['Res']))\n",
    "fig, axs = plt.subplots(1,1,figsize=(4,4))\n",
    "axs.plot(np.arange(len(lambda_res_max))*50, (lambda_res_min), label = f'Min Lambda')\n",
    "axs.plot(np.arange(len(lambda_res_max))*50, (lambda_res_max), label = f'Max Lambda')\n",
    "axs.plot(np.arange(len(lambda_res_mean))*50,(lambda_res_mean), label = f'Mean Lambda')\n",
    "axs.set_xlabel('Epoch')\n",
    "axs.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72e9321",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "Anagnostopoulos, S. J., Toscano, J. D., Stergiopulos, N., & Karniadakis, G. E. (2024). Residual-based attention in physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering, 421, 116805.\n",
    "\n",
    "Link: https://www.sciencedirect.com/science/article/pii/S0045782524000616?dgcid=coauthor"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  },
  "vscode": {
   "interpreter": {
    "hash": "f309334e5f4406e5b2ce0c18f807d883176a73f7f1a0cd119c91872201c4e9da"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
